{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:13.760124Z",
     "start_time": "2025-05-09T01:56:13.736691Z"
    }
   },
   "source": [
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.facecolor'] = 'white'\n",
    "train = pd.read_csv('./train.csv')\n",
    "test = pd.read_csv('./test.csv')\n",
    "train.head()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ],
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 54
  },
  {
   "cell_type": "code",
   "id": "834882a33ba93851",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:14.804281Z",
     "start_time": "2025-05-09T01:56:14.772492Z"
    }
   },
   "source": [
    "# 为了方便处理数据将train和test合并\n",
    "full_data = pd.concat([train, test], axis='rows', ignore_index=True)\n",
    "full_data.describe()\n",
    "# 数据没有明显的异常值"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       PassengerId    Survived       Pclass          Age        SibSp  \\\n",
       "count  1309.000000  891.000000  1309.000000  1046.000000  1309.000000   \n",
       "mean    655.000000    0.383838     2.294882    29.881138     0.498854   \n",
       "std     378.020061    0.486592     0.837836    14.413493     1.041658   \n",
       "min       1.000000    0.000000     1.000000     0.170000     0.000000   \n",
       "25%     328.000000    0.000000     2.000000    21.000000     0.000000   \n",
       "50%     655.000000    0.000000     3.000000    28.000000     0.000000   \n",
       "75%     982.000000    1.000000     3.000000    39.000000     1.000000   \n",
       "max    1309.000000    1.000000     3.000000    80.000000     8.000000   \n",
       "\n",
       "             Parch         Fare  \n",
       "count  1309.000000  1308.000000  \n",
       "mean      0.385027    33.295479  \n",
       "std       0.865560    51.758668  \n",
       "min       0.000000     0.000000  \n",
       "25%       0.000000     7.895800  \n",
       "50%       0.000000    14.454200  \n",
       "75%       0.000000    31.275000  \n",
       "max       9.000000   512.329200  "
      ],
      "text/html": [
       "<div>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>655.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>33.295479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>14.413493</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>51.758668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>328.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>655.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>982.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 55
  },
  {
   "cell_type": "code",
   "id": "954860da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:14.917324Z",
     "start_time": "2025-05-09T01:56:14.905065Z"
    }
   },
   "source": [
    "full_data.info()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "execution_count": 56
  },
  {
   "cell_type": "code",
   "id": "333e7682cfac15d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:15.203721Z",
     "start_time": "2025-05-09T01:56:15.045349Z"
    }
   },
   "source": [
    "import missingno as msno\n",
    "\n",
    "#可视化展示缺失值的分布\n",
    "msno.matrix(full_data, figsize=(10, 6))\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 57
  },
  {
   "cell_type": "code",
   "id": "a34d32f8a9aaad86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:15.487040Z",
     "start_time": "2025-05-09T01:56:15.299727Z"
    }
   },
   "source": [
    "# 港口与生存率之间的关系\n",
    "palette = sns.color_palette('husl', n_colors=train['Embarked'].nunique())\n",
    "sns.barplot(data=train, x='Embarked', y='Survived', palette=palette)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Embarked', ylabel='Survived'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 58
  },
  {
   "cell_type": "code",
   "id": "71bdb9ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:15.583101Z",
     "start_time": "2025-05-09T01:56:15.564600Z"
    }
   },
   "source": [
    "# 计算不同港口登船的乘客，生存率为多少\n",
    "survived_data = train.groupby('Embarked')['Survived']\n",
    "# 生存率计算\n",
    "survived_rate = (\n",
    "    survived_data\n",
    "    .agg([\n",
    "        ('died', lambda x: (x == 0).sum()),\n",
    "        ('survived', lambda x: (x == 1).sum()),\n",
    "        ('total', 'count')\n",
    "    ])\n",
    "    .eval('Survival_rate = survived / total')\n",
    "    .eval('died_rate = died / total')\n",
    ")\n",
    "survived_rate"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          died  survived  total  Survival_rate  died_rate\n",
       "Embarked                                                 \n",
       "C           75        93    168       0.553571   0.446429\n",
       "Q           47        30     77       0.389610   0.610390\n",
       "S          427       217    644       0.336957   0.663043"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>died</th>\n",
       "      <th>survived</th>\n",
       "      <th>total</th>\n",
       "      <th>Survival_rate</th>\n",
       "      <th>died_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>75</td>\n",
       "      <td>93</td>\n",
       "      <td>168</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>0.446429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>47</td>\n",
       "      <td>30</td>\n",
       "      <td>77</td>\n",
       "      <td>0.389610</td>\n",
       "      <td>0.610390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>427</td>\n",
       "      <td>217</td>\n",
       "      <td>644</td>\n",
       "      <td>0.336957</td>\n",
       "      <td>0.663043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 59
  },
  {
   "cell_type": "code",
   "id": "387516a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:15.887774Z",
     "start_time": "2025-05-09T01:56:15.877793Z"
    }
   },
   "source": [
    "pclass_data = train.groupby('Embarked')['Pclass'].value_counts().sort_index(level=[0, 1], ascending=[True, True])\n",
    "pclass_data = pclass_data.to_frame()\n",
    "pclass_data"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 count\n",
       "Embarked Pclass       \n",
       "C        1          85\n",
       "         2          17\n",
       "         3          66\n",
       "Q        1           2\n",
       "         2           3\n",
       "         3          72\n",
       "S        1         127\n",
       "         2         164\n",
       "         3         353"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">C</th>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Q</th>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">S</th>\n",
       "      <th>1</th>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 60
  },
  {
   "cell_type": "code",
   "id": "f5426a04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:16.388931Z",
     "start_time": "2025-05-09T01:56:16.072358Z"
    }
   },
   "source": [
    "# 画图分析对于不同港口不同机票仓位的人生存率\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "embarked_values = pclass_data.index.get_level_values('Embarked').unique()\n",
    "for i, embarked in enumerate(embarked_values):\n",
    "    subset = pclass_data.loc[embarked]\n",
    "    axes[i].bar(subset.index, subset['count'], color=['blue', 'orange', 'green'])\n",
    "    axes[i].set_title(f'Embarked = {embarked}')\n",
    "    axes[i].set_xlabel('Pclass')\n",
    "    axes[i].set_ylabel('count')\n",
    "    axes[i].set_xticks(subset.index)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ],
      "image/png": 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TJ5ve5bXXXsuC8jRf+s6ampqyUez5SOGegK/9HC/y5ZyBXRXzmvjTctwAAO2wcOHCbHTbOx9p+xNPPBEnn3xyHHfccXHbbbeVuqkAsNdJtXjmzJmtz9MI8xSof/vb345Fixa1bn/yySezKVvSHOpDhw6Np556qnXf2rVrY+vWrXmNQgeASmckOgDQbqeddlqcdNJJrc//+Mc/ZouUHXbYYfHZz342PvOZz2SvmTx5chx11FFx/PHHl7S9ALA3GThwYNxzzz3Zfz/60Y/G7NmzY8SIETFkyJDs3wcccEBs3749W1Q01e+0mGhaZDRN67JgwYKYMGFCzJkzJ4YPH55N8wIA/IkQHQBotzSibed5U++6667sNvE0ou3AAw+MSy65JBvxdvHFF8f8+fOF6ABQRKkWp/nP/+mf/ikbkT5y5MiYNWtW7L///vH888/HpZdemoXjp59+evaBd1JbWxuNjY0xZcqU7LVphPrcuXNL/aMAQFkRogMAu+Wtt96KO+64Ixvx9p3vfCebxiUF6MkxxxwTX//610vdRADY66SR5z/+8Y932Z5C8vR4N2mR0SVLlsTy5cujvr4+6urqitBSAOg8hOgAwG5Jc6umsLxfv37ZbeBpSpcWPXv2zBYry8f/zd/J41g5ZrSXcwZyK8R1UQnXVt++fWP06NGlbgYAlCUhOgCwW374wx9mt4Un6dbwnad56datW2zZsiWv9+vTp1fB21jpHDPy5ZyBturqepS6CQBAJyBEBwDy9pvf/CZeeumlbOGxpHfv3rF+/frW/U1NTdGlS5e83nPduk3R3FzwplakNOIxhaGOGe3lnCmsmppq4WuF2LChKbZv31GwawwAqExCdAAgb4sXL85u+W4JyocOHRoPPPBA6/5nn302DjrooLzeMwV7wr38OGbkyzkDu3JNAADvpfo9XwEA8A4///nP4yMf+Ujr8zFjxsSvf/3rePTRR+Ptt9+OW265JUaOHFnSNgIAAEAhCNEBgLykuc6ffvrpOPbYY1u37b///jF16tS48MILY8SIEfHiiy/GRRddVNJ2AgAAQCGYzgUAyMs+++wTzzzzzC7bJ06cmI0+f+GFF2LYsGHRo4f5ggEAAOj8hOgAQMH0798/ewAAAEClKMl0Lvfee2+MGjUq6uvr49Of/nSsXbs2275q1aqYMGFCNDQ0xMyZM6PZCi8AAAAAAOxNIfpLL70U3/nOd+Kmm26KxYsXZ6PVvvzlL8fWrVtj0qRJMWTIkFiwYEGsWbMmFi5cWOzmAQAAAABA6UL0Z599NhuBnsLy97///dnI89/85jexbNmy2Lx5c7Yo2YABA2Ly5Mkxf/78YjcPAAAAAABKF6Iffvjh8fjjj8eKFSti06ZNcdddd8WIESNi5cqVWbjevXv37HWDBg3KRqMDAAAAAMBes7BoCtH/5m/+Js4888zseb9+/bI50m+++ebs3y2qqqqiuro6Nm7cGL179273+1dVdUiz6eScF4U7ho4l7eWcyY/jBAAAAOWp6CH6//7f/zsefvjhuOeee+Kv/uqv4pZbbonPf/7zcfzxx0fXrl3bvLZbt26xZcuWvEL0Pn16dUCr6czq6nqUugkVxTVGvpwzAAAAQGdW9BD9gQceiFNPPTWbuiX5h3/4h/jBD36QjU5fvXp1m9c2NTVFly5d8nr/des2RXPznrezpqZa+FohNmxoiu3bd5S6GRUxSjaFoYW6xqh8zpndO14AAADAXh6i79ixIzZs2NAmKH/zzTejtrY2nnrqqdbta9euja1bt+Y1Cj1JQY2whndyThSOa4x8OWcAAACAzqzoC4sOGzYslixZEt///vdj0aJFcfHFF0ffvn3j05/+dGzevDkWLFiQvW7OnDkxfPjwqKmpKXYTAQAAAACgNCPR07Qta9asidtvvz3+8Ic/xBFHHBHf/va3s2lbGhsbY8qUKTFr1qxsUdG5c+cWu3kAAAAAAFC6EL2qqiouueSS7PFOY8eOzUapL1++PJszva6urtjNAwAAAACA0oXo7yVN7TJ69OhSNwMAAAAAAIo/JzoAAAAAAHQWQnQAAAAAAMhBiA4AAAAAADkI0QEAAAAAIAchOgAAAAAA5CBEBwAAAACAHIToAAAAAACQgxAdAAAAAAByEKIDAAAAAEAOQnQAAAAAAMhBiA4AAAAAADkI0QEAAAAAIAchOgAAAAAA5CBEBwAAAACAHIToAAAAAACQgxAdAAAAAAByEKIDAAAAAEAOQnQAAAAAAMhBiA4AAAAAADkI0QEAAAAAIAchOgAAAAAA5CBEBwAAAACAHIToAAAAAACQgxAdAAAAKsgbb7wRTz/9dGzcuLHUTQGAiiBEBwAAgAqxePHiGDNmTFx55ZUxatSo7HmyatWqmDBhQjQ0NMTMmTOjubm59WueeOKJOPnkk+O4446L2267rYStB4DyJEQHAACACrBp06a49tpr484774xFixbF1VdfHddff31s3bo1Jk2aFEOGDIkFCxbEmjVrYuHChdnXrF+/Pi666KI49dRT4+67786+7vHHHy/1jwIAZUWIDgAAABVg8+bNMW3atDjyyCOz54MHD44NGzbEsmXLsn1Tp06NAQMGxOTJk2P+/PnZa+6///448MAD45JLLomBAwfGxRdf3LoPAPgTIToAAABUgIMPPjjGjx+f/fvtt9+O22+/PcaNGxcrV66M+vr66N69e7Zv0KBB2Wj05LnnnsumcamqqsqeH3PMMbF8+fIS/hQAUH5qS90AAAAAoHBSaH7eeedFly5d4sEHH4ybbrop+vXr17o/BebV1dXZwqNphPphhx3Wuq9nz57x2muv5f09/28GTzuPk+NFezlnILdCXBftfQ8hOgAAAFSQNNL81ltvjeuuuy5bYLR///7RtWvXNq/p1q1bbNmyJWpqatrsa9merz59ehWk7XsLx4t8OWegrbq6HkX9fkJ0AAAAqCBppPnRRx8dM2bMyKZzSXOgr169us1rmpqaspHqvXv3zhYXfef2fK1btymamwvS/IqWRjymMNTxor2cM4VVU1Nd9PCVjrFhQ1Ns376jYNfYezEnOgCQt+uvvz4mTZrU+nzVqlUxYcKEaGhoiJkzZ0azv/ABoOieeOKJrA63SCPMU6Cepmt56qmnWrevXbs2tm7dmgXoQ4cObbPv2WefjYMOOijv751Kv0f7Ho6XR74P50xhjyWVo7mI54UQHQDIe57Vu+66K6644orseeqEp0B9yJAhsWDBgmyhsoULF5a6mQCw1xk4cGDcc889cffdd8err74a3/jGN2LEiBExatSobO7zVKeTOXPmxPDhw7OpXMaMGRO//vWv49FHH80WI73lllti5MiRpf5RAKCsCNEBgHbbsWNHXH311XH++edn86smy5YtyzrmU6dOjQEDBmS3jM+fP7/UTQWAvc6BBx4YN9xwQ9xxxx1x6qmnxptvvhmzZs2K2traaGxsjOnTp8dxxx0XS5cujcsuuyz7mv333z+r4RdeeGEWuL/44otx0UUXlfpHAYCyUvQ50dPItFSg3ykteJJWC7/mmmuy+djSiLbPfOYzxW4eAPBn/OAHP8imbvnEJz6RdcBPOOGEbGR6fX19dO/evXUxszQavRQrq+8tWo6VY0Z7OWcgt0JcF+V0baUg/Mc//vEu28eOHRtLliyJ5cuXZ3W7rq6udd/EiROz0ecvvPBCDBs2LHr0MF8wAJQ0RD/ttNPipJNOan3+xz/+Mc4888xsjrbPfvazWXCeXpNGsR111FFx/PHHF7uJAMC7SAuN3XjjjdkI9N/+9rfxox/9KL773e9mne30QXiLNPdqdXV1bNy4MZtrtb3as5gLbTlm5Ms5A23tbYvL9e3bN0aPHv2u+1J9b7nLDAAocYieFjZJjxZpTtW0WviTTz6Z3Xp2ySWXZJ3viy++OLsVXIgOAOUhjV5Lt4Xffvvt2a3f27Zti9NPPz2bX/Wss85q89pu3brFli1b8grR163bZLGfPFeQd8xoL+dMYdXUVO914Wul2rChKbZv31GwawwAqExFD9F39tZbb2VztaWFT77zne9kc7OlAD055phj4utf/3opmwcA7OR3v/tddvt3CtCTNL9qmrol3fqdpmJ756j1Ll265PX++ayMzp84ZuTLOQO7ck0AAGUdoi9atCgLy9Mt4GlBsjSlS4uePXvGa6+91qnnoqN8OC/2nLlUyZdzJj+d4Ti9733vyz4A31ma1mXatGlx5513tm5bu3ZtbN26Na9R6AAAAFCuShqi//CHP4xLL700+3dNTU2baV5abgPPl1voeCe32haWa4x8OWcqx6hRo2L69OnZ4qInnnhi/PSnP80WFf3Wt74V3/ve97JpXSZMmBBz5syJ4cOHZ7UdAAAAOruShei/+c1v4qWXXso62UkarbbzreC7cxt4Uqh5Hs1zWDkKNc/h3s5cquTLOVN5c6nW1dXFzTffHLNmzYoZM2Zki5PNnj07Dj744GhsbIwpU6Zk+9KionPnzi11cwEAAKBzh+iLFy/OVgVvCcqHDh0aDzzwQOv+Z599Ng466KC839c8j7wb50ThuMbIl3Omsnz4wx+Ou+++e5ftY8eOzRYeXb58eTZvegrcAQAAoBJUl+ob//znP4+PfOQjrc/HjBkTv/71r+PRRx+Nt99+O2655ZYYOXJkqZoHAOQpjUxPH5AL0AEAAKgkJQnR01znTz/9dBx77LGt2/bff/+YOnVqXHjhhTFixIh48cUX46KLLipF8wAAAAAAoHTTueyzzz7xzDPP7LJ94sSJ2ejzF154IYYNGxY9epiTHAAAAACAvXBO9Fz69++fPQAAAAAAYK+dEx0AAAAAAMqdEB0AAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHKozbUDgI5XXV2VPSpZTU1lf167Y0dz9gAAAAAqkxAdoERSeN77L/aJ2prK/lVcV9cjKtm27dti4+tbBOkAAABQoSo7uQEo8xA9BejnLDwnVvxhRambw244qu9RMe+sedn/SyE6AAAAVCYhOkCJpQD9yd89WepmAAAAAPAuKnuiWgAAAAAA2ANCdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAoEI89NBDMXbs2Bg8eHCcccYZsWbNmmx7Y2NjDBo0qPUxbty41q9ZtWpVTJgwIRoaGmLmzJnR3Nxcwp8AAMpPSUP066+/PiZNmtT6XOEGAACA3fPSSy/FtGnTYsqUKbFs2bIYOHBgXHHFFdm+Z555Jm6++eb45S9/mT3uu+++bPvWrVuzfvmQIUNiwYIFWei+cOHCEv8kAFBeShair1y5Mu66667Wgq5wAwAAwO5L/egUoJ9yyilxwAEHxMSJE2PFihWxbdu2WL16dQwbNiz222+/7NGzZ8/sa1LYvnnz5pg6dWoMGDAgJk+eHPPnzy/1jwIAZaUkIfqOHTvi6quvjvPPPz/69++fbVO4AQAAYPedeOKJ8clPfrL1+YsvvhiHHHJIdtd36oefeeaZccwxx8QFF1wQv/3tb1sHuNXX10f37t2z52mql5YpYACAEoboP/jBD7Ii/pd/+ZexdOnSbBS6wg0AAACFkfrZt912W3zqU5+K559/Pg499NCYNWtW3H///VFbWxtXXXVV9ro0mK1fv36tX1dVVRXV1dWxcePGvL5fVZVHex+Ol0e+D+dMYY8llaOqiOdFbRRZU1NT3HjjjdkI9PTJ949+9KP47ne/m91Wlqtw9+7du93v74Lg3Tgv9tzOhRvY1Z5eG64tAKCQUr87DVI7++yzo0uXLjF+/PjWfddcc022+GgK0GtqaqJr165tvrZbt26xZcuWvPriffr0Kmj7K53jRb6cM9BWXV2Pon6/oofoS5YsiTfffDNuv/322H///bO52U4//fRsHvSzzjqrzWsVbjrjRVXpXGOwd/+eaWxsjLlz57Y+T1Owpdqe7jBLU7KlBc0+/vGPx5e+9KXsA3EAoPgee+yxmDdvXtxzzz1ZgP5Offr0yaZ3ee2117L+dpov/Z2D397t6/6cdes2RXPzHje94qU/j1KfyvGivZwzhVVTU71X9d8q2YYNTbF9+46CXWNlF6L/7ne/y6ZtSQF61oDa2mzqlhdeeCHWr19fNoXbRVU5CnVR7e0U7sLze6ZyFOL3THsLd6k988wzcfPNN8exxx6bPU93jbUsDj5y5Mj45je/mQXtaXHwCRMmlLq5ALDXWbt2bba4aFqH7PDDD8+2zZw5MwYPHpwNYEuefPLJrIYffPDBMXTo0Lj33nvbfH2q7fkMZktSH0E/of0cL/LlnIFdFfOaKHqI/r73vS/eeuutNtvStC7Tpk2LO++8s3Wbwk0hOScKxzUG725vuC7S3WNppFqagq1Hj//3AdBDDz3Uujh4um08LQ5+7bXXCtEBoMjSndzpg+00Vcu4ceOygWlJGrg2e/bsOOCAA2L79u0xffr0bJHRVLcbGhqyOp7uDk+1e86cOTF8+PBsmhcAoEQh+qhRo7KCnRYXTSuH//SnP80WFf3Wt74V3/ve9xRuAChTacqWdOt36nT//ve/zzrdqaYXanFws7+0n3UqyJdzBnIrxHVRLtfWI488ki0imh5pKpcWS5cujVNOOSUuvfTSrI+dRqSnD71b7g5Pd5Gl0etp4dE0Qn3nqdsAgBKE6HV1ddlt4Kk4z5gxI/r27Zt9Ip5uI1O4AaB8pQ75oYceGldddVVWz6+77rrs30cccURBFgfvDNPZlBvHjHw5Z6CtSpta76STTornnnvuXfelvnZ6vJs0cj2tcbJ8+fLsg/FU5wGAEoboyYc//OG4++67d9mucANA+Ro/fnz2aHHNNddktfuwww6Lrl277vHi4NZcaD/rVJAv50xhWdekchR7UbJylga4jR49utTNAICyVJIQ/c9RuAGgc+jTp082vUuaXzXNlb6ni4NbcyF/jhn5cs7ArlwTAMB7qX7PVwAARMTMmTNj0aJFrc+ffPLJbNqWNAf6U089tceLgwMAAEA5KruR6ABAeTryyCOzdUzSyPPt27dni4qmRUZHjBgRmzdvtjg4AAAAFUmIDgC0yxlnnJEtLnrppZdmAfnpp58ekydPjtraWouDAwAAULGE6ABAu6WgPD3eyeLgAAAAVCohOgBQEBYHBwAAoBJZWBQAAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAACgnEL0xsbGGDRoUOtj3Lhx2fZVq1bFhAkToqGhIWbOnBnNzc2laB4AAAAAAJQuRH/mmWfi5ptvjl/+8pfZ47777outW7fGpEmTYsiQIbFgwYJYs2ZNLFy4sBTNAwAAAACA0oTo27Zti9WrV8ewYcNiv/32yx49e/aMZcuWxebNm2Pq1KkxYMCAmDx5csyfP7/YzQMAAAAAgNKF6GnKlh07dsSZZ54ZxxxzTFxwwQXx29/+NlauXBn19fXRvXv37HVpmpc0Gh0AAAAAAEqlttjf8Pnnn49DDz00rrrqqqirq4vrrrsu+/cRRxwR/fr1a31dVVVVVFdXx8aNG6N3797tfv+qqg5qOJ2a86Jwx9CxhHe3p9eGawsAAADKU9FD9PHjx2ePFtdcc02MHTs2DjvssOjatWub13br1i22bNmSV4jep0+vgraXzq+urkepm1BRXGOwK79nAAAAoHIVPUR/pz59+mTTuxxwwAHZXOk7a2pqii5duuT1fuvWbYrm5j1vV01NtVCkQmzY0BTbt+8odTM6vTRKNgXohbrG8HumkhTi90zLNQYAAADs5SH6zJkzY/DgwXH66adnz5988sls2pY0B/q9997b+rq1a9fG1q1b8xqFnqRwT8DHOzknCsc1Bu/OdQEAAACVqegh+pFHHhmzZ8/ORp5v3749pk+fni0yOmLEiNi8eXMsWLAgJkyYEHPmzInhw4dHTU1NsZsIAAAAAACZ6iiyM844I0455ZS49NJLY8qUKXHCCSdkC4vW1tZGY2NjFqofd9xxsXTp0rjsssuK3TwAAADotB566KFs3bF0B3jqf69ZsybbvmrVqmzAWkNDQ3aHePNOt9E98cQTcfLJJ2d98dtuu62ErQeA8lT0ED1J4fl//Md/xC9+8Yu48sorY9999822p0K/ZMmSrKA/+OCDcfjhh5eieQAAANDpvPTSSzFt2rSsz71s2bIYOHBgXHHFFdlUqZMmTYohQ4Zkd3+nYH3hwoXZ16xfvz4uuuiiOPXUU+Puu++ORYsWxeOPP17qHwUAykpJQvQ/p2/fvjF69Oioq6srdVMAAACg00jheArQ093faQrViRMnxooVK7JAPU2fOnXq1BgwYEBMnjw55s+fn33N/fffHwceeGBccsklWeh+8cUXt+4DAEo0JzoAAABQeCeeeGKb5y+++GIccsghsXLlyqivr4/u3btn2wcNGtQ6zctzzz2XTeNSVVWVPT/mmGPi61//et7f+/9+Oe08To4X7eWcgdwKcV209z2E6ADAbrnggguyW7/POuusbC7Va665JrslPN0u/pnPfKbUzQOAvVqawiXNb37++edn07z069evdV8KzKurq2Pjxo3ZCPXDDjusdV/Pnj3jtddey/v79enTq2Bt3xs4XuTLOQNt1dX1iGIqSIieFiTZsWNH1NTUFOLtAIAi2p06nm79fuSRR7IQvWUu1RScn3baadkt4kcddVQcf/zxHdpuANgb7G5/+8Ybb8xGnp999tkxe/bs6Nq1a5v93bp1iy1btmTvu/O+lu35WrduU+y0Vil/ZsRjCkMdL9rLOVNYNTXVRQ9f6RgbNjTF9u07CnaNFXxO9K985SvZJ9o7S4uOpDnXAIDyVog6/vrrr2eLgB966KHZc3OpAkBhFKq//dhjj8W8efOyaVm6dOkSvXv3zj703llTU9O77mvZnq8U7nm07+F4eeT7cM4U9lhSOZqLeF7kHaKn1brfWdQPP/zweOWVV/J9KwCgyApRx1OAftJJJ8UHP/jBnHOpLl++vMAtB4DKV4g6vXbt2mxx0auvvjr72mTo0KHx1FNPtXlN+j4pQH/nvmeffTYOOuiggvw8AFAp2j2dyz//8z+33kq2aNGi1gVJ0vP0yfjRRx/dca0EAPZIoep4em0a3fbAAw9EY2Njtq1Qc6laLKn9LDBFvpwzUB6LknV0nU7TsKS1ScaOHRvjxo3LRpUnw4YNy+r1ggULYsKECTFnzpwYPnx4NpXLmDFj4qtf/Wo8+uij0dDQELfcckuMHDlyz34gANhbQ/RUbJM0yiwV9Zb52NJiJAMGDIhvfOMbHddKAGCPFKKOv/XWW9nioelW8xSUtyjUXKoWS8qfY0a+nDPQVrnMi1uo/nZar+T555/PHvfcc0/r9qVLl2YffqcR6rNmzcred+7cudm+/fffP6ZOnRoXXnhh7LvvvtGrV6+YMWNGh/ycAFDxIXpLgT3yyCPj5ptvbtN5BgDKWyHq+E033ZSNhBs9enSb7YWaS9ViSe1ngSny5ZwpLIuSVY5iL0rW0f3tNN1ammbt3fTr1y+WLFmSTblWX18fdXV1rfsmTpyYjT5/4YUXslHrPXo4vwFgt0L0Fp/85Cd3WdUbAOgc9qSOp5FxGzZsyDrXSRptvnjx4uzfxx577B7PpWqxn/w5ZuTLOQO7KqdroqP723379t3lw/AW/fv3zx4AQAFC9GuvvTZbgOTVV1/N5mfb2fvf//583w4AKKI9qeN33XVXbNu2rfV5uh08jWT72Mc+lnXIzaUKAHtGfxsAKiREnzdvXsycOTPefvvtNkU9zd22YsWKQrcPACigPanj73vf+9o8T/OmplvBzaUKAIWhvw0AFRKi33DDDXH55ZfHJz7xid2a7xQAKJ1C1vGdg3JzqQLAntPfBoAKCdFTp/j4449X0AGgE+rIOm4uVQDYM/rbAFCeqvP9giuvvDKuuuqqWL16dce0CADoMOo4AJQvdRoAKmQkemNjY7z++usxfvz42G+//aJnz56t+5YuXVro9gEABaSOA0D5UqcBoEJCdAuFAUDnpY4DQPlSpwGgQkL0fv36dUxLAIAOp44DQPlSpwGgQkL0MWPGRFVVVTQ3N2fP079brFixorCtAwAKSh0HgPKlTgNAhYToK1eubP33li1b4plnnokbb7wxzjvvvEK3DQAoMHUcAMqXOg0A5al6T754n332iWHDhsX3vve9+Pa3v124VgEAHU4dB4DypU4DQIWE6C3S6uHr1q0rxFsBAEWmjgNA+VKnAaATz4neIs3V9tprr8W5555b6LYBAAWmjgNA+VKnAaBCQvQZM2a0eZ4K/EEHHRQDBgwoZLsAgA6gjgNA+VKnAaBCpnP5yEc+kj3S/Gzr16+Pbt26KegA0Emo4wBQvtRpAChPeY9E//3vfx8XXXRR/Od//mf2iXi6tWzgwIFx0003Zc8BgPKljgNA+VKnAaBCRqJfffXVcfTRR8fjjz8eixcvjsceeyyGDBkSV111Vce0EAAoGHUcAMqXOg0AFRKi/+pXv8o+Ge/atWv2PP130qRJ8etf/7oj2gcAFJA6DgDlS50GgAoJ0T/wgQ/Efffd12Zben7EEUcUsl0AQAdQxwGgfKnTAFAhc6J/5StfiQsuuCAWLVoU/fr1i7Vr10ZTU1P8r//1vzqmhQBAwajjAFC+1GkAqJAQPX0y/i//8i/x8MMPx6uvvhof+9jHYvTo0bHvvvt2TAsBgIJRxwGgfKnTAFAh07k8//zzcc4550R1dXV87nOfi+9+97vxiU98Il588cWOaSEAUDDqOACUL3UaACokRE+rhTc0NMTIkSOz53fffXf2yfg111zTEe0DAApIHQeA8qVOA0CFhOgrVqzIPhHv1atX9jzdVvbpT386li9f3hHtAwAKSB0HgPKlTgNAhYTogwYN2mW18B/96EdWCweATkAdB4DypU4DQIUsLJpuL/v85z+fFfK0Wvgrr7wSGzdujFtuuWW3GpBWHj/11FPjrLPOiieeeCK7TW39+vUxadKk+MxnPrNb7wkAFKeOAwCFo04DQHnKO0QfPHhwtlr4z372s2y18PHjx2dztPXs2TPvb37//ffHI488koXoKTi/6KKLsuD8tNNOi8mTJ8dRRx0Vxx9/fN7vCwB0fB0HAApLnQaACgnRk1TAU9C9J15//fWYOXNmHHrooa2B+oEHHhiXXHJJVFVVxcUXXxzz588XogNAgRWijgMAHUOdBoAKmBO9UFKAftJJJ8UHP/jB7Plzzz0Xxx13XBagJ8ccc4zFUwAAAAAA6Hwj0ffU448/Ho899lg88MAD0djYmG3bvHlzHHbYYW0+fX/ttdfyfu//m8FDG86Lwh1DxxLe3Z5eG64tAAAAKE9FD9HfeuutbPHQr3zlK23mdaupqYmuXbu2Pu/WrVts2bIl7/fv06dXwdpKZair61HqJlQU1xjsyu8ZAAAAqFxFD9FvuummOProo7PFUXbWu3fvbHHRFk1NTdGlS5e833/duk3R3Lzn7aypqRaKVIgNG5pi+/YdpW5Gp5dGyaYAvVDXGH7PVJJC/J5pucYAAID8VFdXZY9K7z9Wqh07mrMHlLOih+iLFi2KDRs2xLBhw7LnabT54sWLs38fe+yxra979tln46CDDsr7/VO4J+DjnZwTheMag3fnugAAgOJL4fn+dftEVXVJZiwumkoegNW8Y1us37BFkE5ZK/pvmLvuuiu2bdvW+nzWrFlRX18fH/vYx7LR6Y8++mg0NDTELbfcEiNHjix28wAAAADoRCF6FqA/ek7ExhWlbg756n1UVA2fl/1/FKJTzooeor/vfe9r83zfffeNurq62H///WPq1Klx4YUXZtt69eoVM2bMKHbzAAAAAOhsUoC+4clStwKoUCW/12XnoHzixInZ6PMXXnghm+6lR4/KvVUFAAAAAIDyV/IQ/Z369++fPQAAAAAAoNQqd2lfAAAAAADYQ0J0AAAAAADIQYgOAAAAAAA5CNEBAAAAACAHIToAAAAAAOQgRAcAAAAAgByE6AAAAFAh1q9fH2PGjImXX365dVtjY2MMGjSo9TFu3LjWfatWrYoJEyZEQ0NDzJw5M5qbm0vUcgAoX0J0AAAAqJAAfdKkSfHKK6+02f7MM8/EzTffHL/85S+zx3333Zdt37p1a/b6IUOGxIIFC2LNmjWxcOHCErUeAMqXEB0AyMsbb7wRTz/9dGzcuLHUTQEAdjJ58uQ47bTT2mzbtm1brF69OoYNGxb77bdf9ujZs2e2b9myZbF58+aYOnVqDBgwIPv6+fPnl6j1AFC+hOgAQLstXrw4u0X8yiuvjFGjRmXPE7eCA0DpTZ8+Pc4999w221KN3rFjR5x55plxzDHHxAUXXBC//e1vs30rV66M+vr66N69e/Y8TfWSRqMDAG0J0QGAdtm0aVNce+21ceedd8aiRYvi6quvjuuvv96t4ABQJvr377/Ltueffz4OPfTQmDVrVtx///1RW1sbV111VbYvjULv169f62urqqqiurp6t+42q6ryaO/D8Sr88aQyOF/IVzHPi9q8WwcA7JVSR3vatGlx5JFHZs8HDx4cGzZsaHMreBrJlm4FT2F7GpkOAJTW+PHjs0eLa665JsaOHZvV7pqamujatWub13fr1i22bNkSvXv3zuv79OnTq2Bt3hs4XtBWXV2PUjeBTqauyOeMEB0AaJeDDz64tRP+9ttvx+233x7jxo1zKzgAdCJ9+vTJpnd57bXXsqA8zZe+s6ampujSpUve77tu3aYwm9t7SyMeU4DueBVOTU21ALYCbNjQFNu37+jw7+N8qRwbCnTOtPxefi9CdAAgLyk0P++887IO9oMPPhg33XRTzlvB8xnF5vbKyPtYOWa0l3MGcivEdVHO11ZaqyTdPXb66adnz5988smsTqcPx4cOHRr33ntv62vXrl2bTdOW7yj0JAXCQuH2c7xgV64JyvmcEaIDAHlJI81vvfXWuO6667IFRtP8q4W4FdxtzflzzMiXcwba2htGI6Zp2GbPnh0HHHBAbN++PVt8NC0ymu4gSwuCp2ld0pomaRq2OXPmxPDhw7NpXgCA/0eIDgDkJY00P/roo2PGjBnZdC5pDvRC3Arutub2cys4+XLOFJZbwStHsW8FL4UzzjgjW1z00ksvzcLxNCI91e4kLTLa2NgYU6ZMyRYeTSPU586dW+omA0DZEaIDAO3yxBNPxMMPPxyXX3559jyNPk+B+mGHHVaQW8Hd1pw/x4x8OWdgV5V4TTz33HNtnqeQPD3eTVpkdMmSJbF8+fJsjZO6uroitRIAOo/qUjcAAOgcBg4cGPfcc0/cfffd8eqrr8Y3vvGNGDFiRIwaNar1VvDEreAA0Ln07ds3Ro8eLUAHgByE6ABAuxx44IFxww03xB133BGnnnpqvPnmm9mt3y23gqc5Vo877rhYunRpXHbZZaVuLgAAABSE6VwAgHZLI89//OMf77LdreAAAABUKiE6AFDQW8EBAACgkpjOBQAAAAAAchCiAwAAAABADkJ0AAAAAADIQYgOAAAAAAA5CNEBAAAAACAHIToAAAAAAOQgRAcAAAAAgByE6AAAAAAAkIMQHQAAAAAAchCiAwAAAABADkJ0AAAAAADIQYgOAAAAAAA5lCxEf+ONN+Lpp5+OjRs3lqoJAAAAAABQfiH64sWLY8yYMXHllVfGqFGjsufJqlWrYsKECdHQ0BAzZ86M5ubmUjQPAAAAAAAytVFkmzZtimuvvTbuvPPOOPLII2PhwoVx/fXXx9ixY2PSpEkxcuTI+OY3vxmNjY3ZvhSqAwAAAHuH6uqq7FHJamoqd3bdHTuaswdAJSl6iL558+aYNm1aFqAngwcPjg0bNsSyZcuyfVOnTo3u3bvH5MmTs7BdiA4AAAB7hxSe9+7dI2prKztEr6vrEZVq27bm2LixSZAOVJSih+gHH3xwjB8/Pvv322+/HbfffnuMGzcuVq5cGfX19VmAngwaNCjWrFlT7OYBAAAAJQzRU4B+zjkRK1aUujXk66ijIubN+9OdBEJ0oJIUPURvkULz8847L7p06RIPPvhg3HTTTdGvX7/W/VVV6ZdudbbwaO/evdv9vlWV/WE1u8l5Ubhj6FjCu9vTa8O1BQDw/6QA/cknS90KAChxiJ5Gmt96661x3XXXZQuM9u/fP7p27drmNd26dYstW7bkFaL36dOrA1pLZ1bJt8mVgmsMduX3DAAAAFSukoXoaaT50UcfHTNmzMimc0lzoK9evbrNa5qamrKR6vlYt25TNDcXZpEPoUhl2LChKbZv31HqZnR6aZRsCtALdY3h90wlKcTvmZZrDAAAANjLQ/QnnngiHn744bj88suz52n0eQrUDzvssLj33ntbX7d27drYunVrXqPQkxTuCfh4J+dE4bjG4N25LgAAAKAyVRf7Gw4cODDuueeeuPvuu+PVV1+Nb3zjGzFixIgYNWpUbN68ORYsWJC9bs6cOTF8+PCoqakpdhMBAAAAAKA0IfqBBx4YN9xwQ9xxxx1x6qmnxptvvhmzZs2K2traaGxsjOnTp8dxxx0XS5cujcsuu6zYzQMAAAAAgNLOiZ5Gnv/4xz/eZfvYsWNjyZIlsXz58qivr4+6urpSNA8AAAAAAEq7sGguffv2jdGjR5e6GQAAAAAAUPzpXAAAAAAAoLMQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAqyPr162PMmDHx8ssvt25btWpVTJgwIRoaGmLmzJnR3Nzcuu+JJ56Ik08+OY477ri47bbbStRqAChfQnQAAACooAB90qRJ8corr7Ru27p1a7ZtyJAhsWDBglizZk0sXLiw9fUXXXRRnHrqqXH33XfHokWL4vHHHy/hTwAA5UeIDgC020MPPRRjx46NwYMHxxlnnJF1wt9rdBsAUDyTJ0+O0047rc22ZcuWxebNm2Pq1KkxYMCA7DXz58/P9t1///1x4IEHxiWXXBIDBw6Miy++uHUfAPAnQnQAoF1eeumlmDZtWkyZMiXrjKeO9hVXXPFnR7cBAMU1ffr0OPfcc9tsW7lyZdTX10f37t2z54MGDWr9IPy5557LpnGpqqrKnh9zzDGxfPnyvL9v+vJCPKgchTonnDN7D+cL+SrmeVGbd+sAgL1S6mynAP2UU07Jnk+cODH+7u/+rs3ottQ5T6Pbrr322mxkOgBQXP37999lW6rT/fr1a32eAvPq6urYuHFjtu+www5r3dezZ8947bXX8v6+ffr02oNWU2nq6nqUugl0Ms4Zyv2cEaIDAO1y4okntnn+4osvxiGHHPJnR7flw8iQ/I+VY0Z7OWcgt0JcF+V+bdXU1ETXrl3bbOvWrVts2bJll30t2/O1bt2mKMRsbjU11cK0CrBhQ1Ns376jKN/LOVMZinXOOF8qx4YCnTOphrfng2AhOgCQtzSFy2233Rbnn39+Ns1LrtFtvXv3bvd7GsGWP8eMfDlnoK29JUhJ9Xj16tVttjU1NUWXLl2yfWlx0Xduz1cK0C2Jws6cD+TLOUM5nzNCdAAgbzfeeGM28vzss8+O2bNn5xzdlk+IXqgRbHuDltESjhnt5ZwpLKPYKkexR7GVytChQ+Pee+9tfb527drsA/FUp9O+Bx54oHXfs88+GwcddFCJWgoA5UmIDgDk5bHHHot58+bFPffc0zqCLdfotnwYwZY/x4x8OWdgV3vDNdHQ0JDNfZ4WAE9rlsyZMyeGDx+eTeUyZsyY+OpXvxqPPvpo9rpbbrklRo4cWeomA0BZEaIDAO2WRq6lxUWvvvrqOPzww99zdBsAUHq1tbXR2NiY1fBZs2Zl067NnTs327f//vtni4NfeOGFse+++0avXr1ixowZpW4yAJQVIToA0C5pepZJkybF2LFjY9y4cdlo82TYsGE5R7cBAKXx3HPPtXme6veSJUti+fLl2YLgdXV1rfsmTpyYjT5/4YUXsrreo4fpigBgZ0J0AKBdHnnkkXj++eezR5rKpcXSpUtzjm4DAMpH3759Y/To0e+6r3///tkDANiVEB0AaJeTTjppl1FtLfr165dzdBsAAAB0ZkJ0AKDDR7cBAABAZ1Vd6gYAAAAAAEC5EqIDAAAAAEAOQnQAAAAAAMhBiA4AAAAAAOUUoj/00EMxduzYGDx4cJxxxhmxZs2abPuqVatiwoQJ0dDQEDNnzozm5uZSNA8AAAAAAEoTor/00ksxbdq0mDJlSixbtiwGDhwYV1xxRWzdujUmTZoUQ4YMiQULFmTB+sKFC4vdPAAAAAAAKF2InsLxFKCfcsopccABB8TEiRNjxYoVWaC+efPmmDp1agwYMCAmT54c8+fPL3bzAAAAAACgVW0U2Yknntjm+YsvvhiHHHJIrFy5Murr66N79+7Z9kGDBrVO8wIAAAAAAHtFiL6zNIXLbbfdFueff342zUu/fv1a91VVVUV1dXVs3Lgxevfu3e73rKrqoMbSqTkvCncMHUt4d3t6bbi2AAAAoDyVNES/8cYbs5HnZ599dsyePTu6du3aZn+3bt1iy5YteYXoffr06oCW0pnV1fUodRMqimsMduX3DAAAAFSukoXojz32WMybNy/uueee6NKlSxaUr169us1rmpqasn35WLduUzQ373n7amqqhSIVYsOGpti+fUepm9HppVGyKUAv1DWG3zOVpBC/Z1quMQAAAKC8lCREX7t2bba46NVXXx2HH354tm3o0KFx7733tnlNmu4ln1HoSQr3BHy8k3OicFxj8O5cFwAAAFCZqov9DdP0LJMmTYqxY8fGuHHjstHm6TFs2LDYvHlzLFiwIHvdnDlzYvjw4VFTU1PsJgIAAAAAQGlGoj/yyCPx/PPPZ480lUuLpUuXRmNjYzZCfdasWdmionPnzi128wAAAAAAoHQh+kknnRTPPffcu+7r169fLFmyJJYvXx719fVRV1dX7OYBAAAAAEDpFxbNpW/fvjF69OhSNwMAAAAAAIo/JzoAAAAAAHQWQnQAAAAAAMhBiA4AAAAAADkI0QEAAAAAIAchOgAAAAAA5CBEBwAAAACAHGpz7QDyV11dlT0qWU1NZX/2tmNHc/YAAAAAgESIDgWSwvP96/aJqurKvqzq6npEJWvesS3Wb9giSAcAAAAgU9lpHxQ5RM8C9EfPidi4otTNYXf0Piqqhs/L/l8K0QEAAABIhOhQaClA3/BkqVsBAAAAABRAZU9uDAAAAAAAe0CIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAAAAQA5CdAAAAAAAyEGIDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAECFa2xsjEGDBrU+xo0bl21ftWpVTJgwIRoaGmLmzJnR3Nxc6qYCQNkRogMAeVm/fn2MGTMmXn755dZtOuAAUN6eeeaZuPnmm+OXv/xl9rjvvvti69atMWnSpBgyZEgsWLAg1qxZEwsXLix1UwGg7AjRAYC8AvTU2X7llVdat+mAA0B527ZtW6xevTqGDRsW++23X/bo2bNnLFu2LDZv3hxTp06NAQMGxOTJk2P+/Pmlbi4AlB0hOgDQbqlzfdppp7XZpgMOAOUt3TG2Y8eOOPPMM+OYY46JCy64IH7729/GypUro76+Prp37569Lk3zkj4M3x1VVYV5UDkKdU44Z/YezhfyVczzojbv1gEAe63p06dH//7942tf+1rrtkJ1wP1Rm/+xcsxoL+cM5FaI66Lcr63nn38+Dj300Ljqqquirq4urrvuuuzfRxxxRPTr16/1dVVVVVFdXR0bN26M3r175/U9+vTp1QEtp7Oqq+tR6ibQyThnKPdzRogOALRbCtDfKY1CL0QHXOc7f44Z+XLOwN4Z2owfPz57tLjmmmti7Nixcdhhh0XXrl3bvLZbt26xZcuWvEP0des2RSGWRKmpqd5r/r9Usg0bmmL79h1F+V7OmcpQrHPG+VI5NhTonEkfhLfnb2QhOgCwR2pqagrSAS9U53tv0PKHnmNGezlnCksHvHIUuwNeLvr06ZNN73LAAQdkc6XvrKmpKbp06ZL3e6bfLX6/sDPnA/lyzlDO54wQHQDYIykoL0QHXOc7f44Z+XLOwK72hmti5syZMXjw4Dj99NOz508++WR211iagu3ee+9tfd3atWuzBcPzHYUOAJXOwqIAwB4ZOnRoPPXUU63PdcABoLwceeSRMXv27HjsscfikUceyaZzSYuMjhgxIpuWbcGCBdnr5syZE8OHD8/uMgMAyiBEX79+fYwZMyZefvnlNiuGT5gwIRoaGrJPypv3hiEBANDJpbqtAw4A5euMM86IU045JS699NKYMmVKnHDCCdnCorW1tdHY2JgtHH7cccfF0qVL47LLLit1cwGg7NSWKkCfNGlSvPLKK63b0oi1tG3kyJHxzW9+MyvkCxcuzEJ1AKB8tXTAU6d81qxZ2e3hc+fOLXWzAICdpDqdHu+UFhhdsmRJLF++POrr66Ourq4k7QOAclaSEH3y5Mlx2mmnxdNPP926bdmyZdkotqlTp0b37t2z11x77bVCdAAoQ88991yb5zrgANB59e3bN0aPHl3qZgBA2SrJdC7pVrFzzz23zbaVK1dmne4UoCdpgZM1a9aUonkAwB50wAXoAAAAVJKSjETv37//LtvSKPR+/fq1Pq+qqspuB9+4cWNeC5NVVRWsmVQQ5wX5cs5Q7HPGOQcAAADlqSQh+rtJi4917dq1zbZu3brFli1b8grR+/Tp1QGtozOrq+tR6ibQyThnyJdzBgAAACpX2YToKShfvXp1m21NTU3RpUuXvN5n3bpN0dy85+2pqakWilSIDRuaYvv2HR3+fZwzlcM5QynOmTQS3QfBAAAAUH7KJkQfOnRo3Hvvva3P165dG1u3bs1rFHqSAvRChOhUFucE+XLOkC/nDAAAAFSmkiws+m4aGhqyedEXLFiQPZ8zZ04MHz48m+YFAAAAAAD26pHotbW10djYGFOmTIlZs2Zli4rOnTu31M0CAAAAAGAvVtIQ/bnnnmvzfOzYsbFkyZJYvnx51NfXR11dXcnaBgAAAAAAZTMSvUXfvn1j9OjRpW4GAAAAAACUz5zoAAAAAABQboToAAAAAACQgxAdAAAAAAByEKIDAAAAAEAOQnQAAAAAAMhBiA4AAAAAADkI0QEAAAAAIAchOgAAAAAA5CBEBwAAAACAHIToAAAAAACQgxAdAAAAAAByEKIDAAAAAEAOQnQAAAAAAMhBiA4AAAAAADkI0QEAAAAAIAchOgAAAAAA5CBEBwAAAACAHIToAAAAAACQgxAdAAAAAAByEKIDAAAAAEAOQnQAAAAAAMihNtcOAADYW1RXV2WPSldTU7ljaHbsaM4eAABQaEJ0AAD2aik8379un6iqrvw/jevqekSlat6xLdZv2CJIBwCg4Cq/pwAAAO8RomcB+qPnRGxcUermsDt6HxVVw+dl/y+F6AAAFJoQHQAAkhSgb3iy1K0AAADKTOVOiggAAAAAAHtIiA4AAAAAADmYzgUAqDhpXuT0qHQ1NZU7HiLNa21uawAAoBwI0QGAipLC8969e0RtbeWH6HV1PaJSbdvWHBs3NgnSAQCAkhOiAwAVF6KnAP2ccyJWrCh1a9gdRx0VMW/en+4mEKIDAAClJkQHACpSCtCffLLUrQAAAKCzq9yJNAEAAAAAYA8J0QEAAAAAIAchOgAAAAAAdJYQfdWqVTFhwoRoaGiImTNnRnOzxaQAoDNQwwGg81LHAaCThOhbt26NSZMmxZAhQ2LBggWxZs2aWLhwYambBQC8BzUcADovdRwAOlGIvmzZsti8eXNMnTo1BgwYEJMnT4758+eXulkAwHtQwwGg81LHAaAThegrV66M+vr66N69e/Z80KBB2SfgAEB5U8MBoPNSxwHgz6uNMpI++e7Xr1/r86qqqqiuro6NGzdG79692/Ue1dURhZy67UMfiujRo3DvR/EMGtT2vCia/T8UUeuk6ZT2G1SSc+ZDB38oenRxznRGgw4o3DlTVRWdmhpOIanh5E0Np4Q1PFHH1XFKXMMTdbxzUsPpJDW8rEL0mpqa6Nq1a5tt3bp1iy1btrS7cO+/f6+CtumWWwr6dpRAXV2Rfyke56Tp7Ip9ztwy3jnT2RX990wZUsPpCGo4+VLDyZca/ifqOBVxbanjnZoaTrmfM2U1nUsqzuvXr2+zrampKbp06VKyNgEA700NB4DOSx0HgE4Uog8dOjSeeuqp1udr167NVglv7yffAEBpqOEA0Hmp4wDQiUL0hoaGbC62BQsWZM/nzJkTw4cPz24tAwDKlxoOAJ2XOg4Af15Vc3Mhl/7Yc0uXLo0pU6Zk86+lhUzmzp0bhx9+eKmbBQC8BzUcADovdRwAOlGInvzhD3+I5cuXR319fdTV1ZW6OQBAO6nhANB5qeMA0IlCdAAAAAAAKAdlNSc6AAAAAACUEyE6AB3ijTfeiKeffjo2btxY6qYAAHlSxwGgc1LDO4YQfS+1fv36GDNmTLz88sulbgqdwEMPPRRjx46NwYMHxxlnnBFr1qwpdZMoc4sXL85+x1x55ZUxatSo7DlQGGo4+VDD2R3qOHQcdZx8qOPkSw3vOEL0vbRoT5o0KV555ZVSN4VO4KWXXopp06bFlClTYtmyZTFw4MC44oorSt0sytimTZvi2muvjTvvvDMWLVoUV199dVx//fWlbhZUBDWcfKjh7A51HDqOOk4+1HHypYZ3LCH6Xmjy5Mlx2mmnlboZdBLpk+5UtE855ZQ44IADYuLEibFixYpSN4sytnnz5uyPvSOPPDJ7nkZNbNiwodTNgoqghpMPNZzdoY5Dx1HHyYc6Tr7U8I5V28HvTxmaPn169O/fP772ta+Vuil0AieeeGKb5y+++GIccsghJWsP5e/ggw+O8ePHZ/9+++234/bbb49x48aVullQEdRw8qGGszvUceg46jj5UMfJlxresYToe6FUtGF3bN26NW677bY4//zzS90UOoGVK1fGeeedF126dIkHH3yw1M2BiqCGs7vUcPKljkPhqePsLnWcfKjhHcN0LkC73XjjjdG9e/c4++yzS90UOoFBgwbFrbfemo2WSIuaAFA6ajj5UscByoc6Tj7U8I5hJDrQLo899ljMmzcv7rnnnuzTTHgvVVVVcfTRR8eMGTOyW8jeeOON2G+//UrdLIC9jhrO7lDHAcqDOk6+1PCOYSQ68J7Wrl2bLWiSVnY+/PDDS90cytwTTzwRM2fObH3etWvXrIhXVys5AMWmhpMvdRygfKjj5EMN71hGogN/1pYtW2LSpEkxduzY7BPMpqambPu+++6b/TKGdxo4cGA2SiL996Mf/WjMnj07RowYET179ix10wD2Kmo4u0MdBygP6jj5UsM7lo8igD/rkUceieeffz77RfyhD32o9fHKK6+UummUqQMPPDBuuOGGuOOOO+LUU0+NN998M2bNmlXqZgHsddRwdoc6DlAe1HHypYZ3rKrm5ubmDv4eAAAAAADQKRmJDgAAAAAAOQjRAQAAAAAgByE6AAAAAADkIEQHAAAAAIAchOgAAAAAAJCDEB0AAAAAAHIQogMAAAAAQA5CdKhgv/jFL2LQoEHZY8iQIXH66afHz3/+83Z97csvv5x9HQBQfGo4AHRe6jhUntpSNwDoWD179oyHH344tmzZEj/72c/iC1/4QvzkJz+Jgw46qNRNAwD+DDUcADovdRwqi5HoUOGqqqpiv/32iwMPPDA+8YlPRL9+/eKXv/xlqZsFALwHNRwAOi91HCqLEB32MjU1NfH222/Hs88+G5/85Cfj2GOPjU996lOxevXqdn39r371qzjzzDOjvr4+Pv7xj8fzzz/fuu/RRx+NU045JduX3vM3v/lN674HHnggxowZEx/84AfjggsuiPXr13fIzwcAlUoNB4DOSx2Hzk2IDnuRf//3f48XXnghjjrqqPjc5z4Xo0aNym4nGzp0aFx22WXv+fU7duyIL37xizFu3Lh46KGHoqGhIWbOnNm6/x//8R/jrLPOyt7zsMMOi29961vZ9s2bN8eXv/zlmDJlSlbA0x8Pt912W4f+rABQSdRwAOi81HHo/MyJDhVu06ZNMWzYsHjrrbeia9eucdVVV2WfdKfbyi6++OLsNX//93+fFfX2uO+++6J3796xcuXKeOONN+LFF19s3detW7fYtm1btn/69OnZv5Pa2trWT93TrWzf/e53sz8CAIDc1HAA6LzUcagsRqJDhevRo0f88z//c/Zp9X/8x3/E2WefHa+++mo2H1uLVGjTrV/vpbq6Or7//e/HCSecEF/96lezwr1zAb7++uuzVcg/+tGPxrnnntt6W9o+++wT3/jGN+Luu++Ov/7rv46LLroofve733XQTwwAlUENB4DOSx2HyiJEhwqXim0q0mkF8LSwSXLwwQfHK6+80vqapqamOO200+IPf/jDn32vVJTnz58fDz74YPbfNA9bizfffDO2b9+e3Rr2+OOPx4c//OHstrHk9ddfjwMOOCB+8IMfZHO11dXVxde+9rUO+5kBoBKo4QDQeanjUFmE6LAXGj16dGzcuDHmzJmTfQqdbulKRTcV1z8nFfgkfeqdFjW57rrrorm5OduWvj4tUnL//ffHunXrsu1pW5Kef/rTn45ly5ZlRbzl9QBAftRwAOi81HHovITosBfq1atX3HLLLfGv//qvcfLJJ8fTTz8d3/72t1s/Hc8l3To2cuTIbMGSr3zlK/GJT3wiXnvttfiv//qv6NmzZ3YLWfojIC128vDDD8e1116bfV1a2OTyyy/PvibtS3O3felLXyrSTwsAlUMNB4DOSx2HzququeWjKwAAAAAAoA0j0QEAAAAAIAchOgAAAAAA5CBEBwAAAACAHIToAAAAAACQgxAdAAAAAAByEKIDAAAAAEAOQnQAAAAAAMhBiA4AAAAAADkI0QEAAAAAIAchOgAAAAAA5CBEBwAAAACAeHf/P+CRH3PyyfZ5AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 61
  },
  {
   "cell_type": "code",
   "id": "d3b157d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:16.620038Z",
     "start_time": "2025-05-09T01:56:16.391178Z"
    }
   },
   "source": [
    "# Parch与Survived的关系\n",
    "palette = sns.color_palette(\"husl\", n_colors=train['Parch'].nunique())\n",
    "sns.barplot(data=train, x='Parch', y='Survived', palette=palette)\n",
    "#当同行的父母及子女数量适中时生存率高"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Parch', ylabel='Survived'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 62
  },
  {
   "cell_type": "code",
   "id": "dbe546c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:17.099004Z",
     "start_time": "2025-05-09T01:56:16.817814Z"
    }
   },
   "source": [
    "# SibSP与Survived的关系\n",
    "palette = sns.color_palette('husl', n_colors=train['SibSp'].nunique())\n",
    "sns.barplot(data=train, x='SibSp', y='Survived', palette=palette)\n",
    "# 当同行的同辈数量适中是生存率较高"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SibSp', ylabel='Survived'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 63
  },
  {
   "cell_type": "code",
   "id": "9423cebd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:17.290127Z",
     "start_time": "2025-05-09T01:56:17.101430Z"
    }
   },
   "source": [
    "# Pclass与Survived的关系\n",
    "palette = sns.color_palette('husl', n_colors=train['Pclass'].nunique())\n",
    "sns.barplot(data=train, x='Pclass', y='Survived', palette=palette)\n",
    "#乘客客舱等级越高生存率越高"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='Survived'>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 64
  },
  {
   "cell_type": "code",
   "id": "0d7dd719",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:17.525764Z",
     "start_time": "2025-05-09T01:56:17.364496Z"
    }
   },
   "source": [
    "# Sex与Survived的关系\n",
    "palette = sns.color_palette('husl', n_colors=train['Sex'].nunique())\n",
    "sns.barplot(data=train, x='Sex', y='Survived', palette=palette)\n",
    "# 女性的生存率远高于男性"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Survived'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 65
  },
  {
   "cell_type": "code",
   "id": "89bf68cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:17.894377Z",
     "start_time": "2025-05-09T01:56:17.528249Z"
    }
   },
   "source": [
    "ageFacet = sns.FacetGrid(train, hue='Survived', aspect=3)\n",
    "\n",
    "ageFacet.map(sns.kdeplot, 'Age', shade=True)\n",
    "ageFacet.set(xlim=(0, train['Age'].max()))\n",
    "ageFacet.add_legend()\n",
    "# 乘客年龄段在20-30岁时生存率较高"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bf01390e10>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 966.861x300 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 66
  },
  {
   "cell_type": "code",
   "id": "5c64c552",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:18.238952Z",
     "start_time": "2025-05-09T01:56:17.897020Z"
    }
   },
   "source": [
    "# Fare与Survived的关系\n",
    "ageFacet = sns.FacetGrid(train, hue='Survived', aspect=3)  # aspect每个图片的纵横比\n",
    "ageFacet.map(sns.kdeplot, 'Fare', shade=True)\n",
    "ageFacet.set(xlim=(0, 150))\n",
    "ageFacet.add_legend()\n",
    "#当票价低时乘客生存率较低，票价高生存率越高"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bf01510e10>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 966.861x300 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 67
  },
  {
   "cell_type": "markdown",
   "id": "ddfd63d1",
   "metadata": {},
   "source": [
    "## 缺失值补充"
   ]
  },
  {
   "cell_type": "code",
   "id": "74a609a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:18.249364Z",
     "start_time": "2025-05-09T01:56:18.240891Z"
    }
   },
   "source": [
    "#Cabin缺失值补充\n",
    "full_data['Cabin'].isnull().value_counts()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin\n",
       "True     1014\n",
       "False     295\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 68
  },
  {
   "cell_type": "code",
   "id": "26d1cd7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:18.256206Z",
     "start_time": "2025-05-09T01:56:18.251957Z"
    }
   },
   "source": [
    "# 由于Cabin缺失值较多 直接删除\n",
    "del full_data['Cabin']"
   ],
   "outputs": [],
   "execution_count": 69
  },
  {
   "cell_type": "code",
   "id": "40d35de1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:18.266871Z",
     "start_time": "2025-05-09T01:56:18.259149Z"
    }
   },
   "source": [
    "# 补全Embarked 信息\n",
    "print(full_data['Embarked'].value_counts())\n",
    "#由于港口S的数量最多，所以缺失值为S的可能性最大\n",
    "full_data['Embarked'] = full_data['Embarked'].fillna('S')\n",
    "full_data['Embarked'].head()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embarked\n",
      "S    914\n",
      "C    270\n",
      "Q    123\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    S\n",
       "1    C\n",
       "2    S\n",
       "3    S\n",
       "4    S\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 70
  },
  {
   "cell_type": "code",
   "id": "ec459449",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:18.283775Z",
     "start_time": "2025-05-09T01:56:18.270372Z"
    }
   },
   "source": [
    "full_data[full_data['Fare'].isnull()]\n",
    "full_data['Fare'] = full_data['Fare'].map(lambda x: np.log(x) if x > 0 else x)\n",
    "# 由于这个缺失值只有一个直接分析这个数据的特征 将和他有共同特征的取平均值\n",
    "price = full_data[(full_data['Pclass'] == 3) & (full_data['Embarked'] == 'S')]['Fare'].mean()\n",
    "\n",
    "full_data['Fare'] = full_data['Fare'].fillna(price)\n",
    "full_data['Fare'].info()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Series name: Fare\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "1309 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 10.4 KB\n"
     ]
    }
   ],
   "execution_count": 71
  },
  {
   "cell_type": "code",
   "id": "53ca60e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:18.303750Z",
     "start_time": "2025-05-09T01:56:18.285334Z"
    }
   },
   "source": [
    "numeric_data = full_data.select_dtypes(include=[np.number])\n",
    "numeric_data[numeric_data['Age'].notnull()].corr()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000  0.029340 -0.064097  0.028814 -0.050700 -0.021096   \n",
       "Survived        0.029340  1.000000 -0.359653 -0.077221 -0.017358  0.093317   \n",
       "Pclass         -0.064097 -0.359653  1.000000 -0.408106  0.047221  0.017224   \n",
       "Age             0.028814 -0.077221 -0.408106  1.000000 -0.243699 -0.150917   \n",
       "SibSp          -0.050700 -0.017358  0.047221 -0.243699  1.000000  0.374456   \n",
       "Parch          -0.021096  0.093317  0.017224 -0.150917  0.374456  1.000000   \n",
       "Fare            0.038721  0.342920 -0.741395  0.194035  0.294004  0.318137   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.038721  \n",
       "Survived     0.342920  \n",
       "Pclass      -0.741395  \n",
       "Age          0.194035  \n",
       "SibSp        0.294004  \n",
       "Parch        0.318137  \n",
       "Fare         1.000000  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029340</td>\n",
       "      <td>-0.064097</td>\n",
       "      <td>0.028814</td>\n",
       "      <td>-0.050700</td>\n",
       "      <td>-0.021096</td>\n",
       "      <td>0.038721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0.029340</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.359653</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.017358</td>\n",
       "      <td>0.093317</td>\n",
       "      <td>0.342920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.064097</td>\n",
       "      <td>-0.359653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.408106</td>\n",
       "      <td>0.047221</td>\n",
       "      <td>0.017224</td>\n",
       "      <td>-0.741395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.028814</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.408106</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.243699</td>\n",
       "      <td>-0.150917</td>\n",
       "      <td>0.194035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.050700</td>\n",
       "      <td>-0.017358</td>\n",
       "      <td>0.047221</td>\n",
       "      <td>-0.243699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.374456</td>\n",
       "      <td>0.294004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.021096</td>\n",
       "      <td>0.093317</td>\n",
       "      <td>0.017224</td>\n",
       "      <td>-0.150917</td>\n",
       "      <td>0.374456</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.038721</td>\n",
       "      <td>0.342920</td>\n",
       "      <td>-0.741395</td>\n",
       "      <td>0.194035</td>\n",
       "      <td>0.294004</td>\n",
       "      <td>0.318137</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 72
  },
  {
   "cell_type": "code",
   "id": "572bf7d2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:18.325671Z",
     "start_time": "2025-05-09T01:56:18.311295Z"
    }
   },
   "source": [
    "agePre = full_data[['Pclass','SibSp','Parch','Fare','Age']]\n",
    "agePre"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Pclass  SibSp  Parch      Fare   Age\n",
       "0          3      1      0  1.981001  22.0\n",
       "1          1      1      0  4.266662  38.0\n",
       "2          3      0      0  2.070022  26.0\n",
       "3          1      1      0  3.972177  35.0\n",
       "4          3      0      0  2.085672  35.0\n",
       "...      ...    ...    ...       ...   ...\n",
       "1304       3      0      0  2.085672   NaN\n",
       "1305       1      0      0  4.690430  39.0\n",
       "1306       3      0      0  1.981001  38.5\n",
       "1307       3      0      0  2.085672   NaN\n",
       "1308       3      1      1  3.107198   NaN\n",
       "\n",
       "[1309 rows x 5 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.690430</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.107198</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 73
  },
  {
   "cell_type": "code",
   "id": "d8bc1a7a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:20.079229Z",
     "start_time": "2025-05-09T01:56:18.363720Z"
    }
   },
   "source": [
    "#拆分实验集和预测集\n",
    "ageKnown = agePre[agePre['Age'].notnull()]  # 根据非空数据，规律\n",
    "ageUnKnown = agePre[agePre['Age'].isnull()]  # 空数据，填充\n",
    "\n",
    "#生成实验数据的特征和标签\n",
    "ageKnown_X = ageKnown.drop(['Age'], axis=1)\n",
    "ageKnown_y = ageKnown['Age']\n",
    "\n",
    "#生成预测数据的特征\n",
    "ageUnKnown_X = ageUnKnown.drop(['Age'], axis=1)\n",
    "\n",
    "#利用随机森林构建模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rfr = RandomForestRegressor(random_state=42, n_estimators=1000, n_jobs=-1)\n",
    "rfr.fit(ageKnown_X, ageKnown_y)\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=1000, n_jobs=-1, random_state=42)"
      ],
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=1000, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_estimators=1000, n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 74
  },
  {
   "cell_type": "code",
   "id": "d6df0097",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:20.467555Z",
     "start_time": "2025-05-09T01:56:20.082591Z"
    }
   },
   "source": [
    "score = rfr.score(ageKnown_X, ageKnown_y)\n",
    "print('模型预测年龄得分是：', score)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型预测年龄得分是： 0.6365650553801493\n"
     ]
    }
   ],
   "execution_count": 75
  },
  {
   "cell_type": "code",
   "id": "0f9b7c40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:20.747343Z",
     "start_time": "2025-05-09T01:56:20.471434Z"
    }
   },
   "source": [
    "ageUnKnown_predict = rfr.predict(ageUnKnown_X)\n",
    "full_data.loc[full_data['Age'].isnull(), ['Age']] = ageUnKnown_predict\n"
   ],
   "outputs": [],
   "execution_count": 76
  },
  {
   "cell_type": "code",
   "id": "2a802794",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:20.760986Z",
     "start_time": "2025-05-09T01:56:20.751191Z"
    }
   },
   "source": [
    "full_data.info()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1309 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1309 non-null   float64\n",
      " 10  Embarked     1309 non-null   object \n",
      "dtypes: float64(3), int64(4), object(4)\n",
      "memory usage: 112.6+ KB\n"
     ]
    }
   ],
   "execution_count": 77
  },
  {
   "cell_type": "markdown",
   "id": "4719c9f9",
   "metadata": {},
   "source": [
    "Mr 先生 \\\n",
    "Miss 未婚女性 \\\n",
    "Mrs 已婚女性 \\\n",
    "Master 未成年男孩\n",
    "\n",
    "Dr 医生或者博士 \\\n",
    "Rev 牧师 \\\n",
    "Major 少校 \\\n",
    "Capt 船长 \n",
    "\n",
    "\n",
    "Lady/Sir 爵士 \\\n",
    "the Countess 女伯爵 \\\n",
    "JonkHeer 荷兰贵族 \\\n",
    "Mile 未婚女性 \\\n",
    "Mme 未婚女性 \n",
    "\n",
    "Ms 女士 \\\n",
    "Don 先生\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "id": "2cb15293",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:20.776077Z",
     "start_time": "2025-05-09T01:56:20.763029Z"
    }
   },
   "source": [
    "# 旅客姓名数据中包含头衔信息，不同头衔也可以反映旅客的身份\n",
    "# 而不同身份的旅客其生存率有可能会出现较大差异。因此我们通过Name特征提取旅客头衔Title信息\n",
    "# 并分析Title与Survived之间的关系\n",
    "full_data['Title'] = full_data['Name'].map(lambda x: x.split(',')[1].split('.')[0].strip())\n",
    "full_data['Title']\n",
    "#将差不多的职业整合到一起\n",
    "titleDict = {\n",
    "    'Mr': 'Mr',\n",
    "    'Mlle': 'Miss',\n",
    "    'Miss': 'Miss',\n",
    "    'Master': 'Master',\n",
    "    'Jonkheer': 'Master',\n",
    "    'Mme': 'Mrs',\n",
    "    'Ms': 'Mrs',\n",
    "    'Mrs': 'Mrs',\n",
    "    'Don': 'Royalty',\n",
    "    'Sir': 'Royalty',\n",
    "    'the Countess': 'Royalty',\n",
    "    'Dona': 'Royalty',\n",
    "    'Lady': 'Royalty',\n",
    "    'Capt': 'Officer',\n",
    "    'Col': 'Officer',\n",
    "    'Major': 'Officer',\n",
    "    'Dr': 'Officer',\n",
    "    'Rev': 'Officer'\n",
    "}\n",
    "full_data['Title'] = full_data['Title'].map(titleDict)\n",
    "full_data['Title'].value_counts()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Mr         757\n",
       "Miss       262\n",
       "Mrs        200\n",
       "Master      62\n",
       "Officer     23\n",
       "Royalty      5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 78
  },
  {
   "cell_type": "code",
   "id": "3dd2ace4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:21.043314Z",
     "start_time": "2025-05-09T01:56:20.778078Z"
    }
   },
   "source": [
    "# Title与Survived的关系\n",
    "palette = sns.color_palette('husl', n_colors=full_data['Title'].nunique())\n",
    "sns.barplot(data=full_data, x='Title', y='Survived', palette=palette)\n",
    "# 身份为Mr 和 Officer的乘客生存率明显较低"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Title', ylabel='Survived'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 79
  },
  {
   "cell_type": "code",
   "id": "73c63871",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:21.388511Z",
     "start_time": "2025-05-09T01:56:21.045950Z"
    }
   },
   "source": [
    "# 观察家庭成员数量对生存率的影响 \n",
    "# SibSp ： 泰坦尼克号上同行的兄弟姐妹和配偶\n",
    "# Parch : 同行的家长和孩子\n",
    "full_data['familyNum'] = full_data['Parch'] + full_data['SibSp'] + 1\n",
    "palette = sns.color_palette('husl', n_colors=full_data['familyNum'].nunique())\n",
    "sns.barplot(data=full_data, x='familyNum', y='Survived', palette=palette)\n",
    "# 家庭成员在2-4人时乘客生存率较高，当没有家庭成员同行或家庭成员人数过多时生存率较低"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='familyNum', ylabel='Survived'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 80
  },
  {
   "cell_type": "code",
   "id": "02e37ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:21.402778Z",
     "start_time": "2025-05-09T01:56:21.391947Z"
    }
   },
   "source": [
    "#我们按照家庭成员人数多少，将家庭规模分为小(0)、中(1)、大(2)三类：\n",
    "def familysize(familyNum):\n",
    "    if familyNum == 1:\n",
    "        return 0\n",
    "    elif (familyNum >= 2) & (familyNum <= 4):\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "\n",
    "\n",
    "full_data['familySize'] = full_data['familyNum'].map(familysize)\n",
    "full_data['familySize'].value_counts()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "familySize\n",
       "0    790\n",
       "1    437\n",
       "2     82\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 81
  },
  {
   "cell_type": "code",
   "id": "e58fb9fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:21.608109Z",
     "start_time": "2025-05-09T01:56:21.405210Z"
    }
   },
   "source": [
    "#查看familySize与Survived\n",
    "palette = sns.color_palette('husl', n_colors=full_data['familySize'].nunique())\n",
    "sns.barplot(data=full_data, x='familySize', y='Survived', palette=palette)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='familySize', ylabel='Survived'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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tQQIA0Fk4bRsAyJ7TtgGA7DltGwDIntO2AYDsOW0bAMie07YBgOw5bRsAqMxVTmnOzMqVK3d8awAAShVorrjiimJCMABAtoEmrWa6+eabbaoHAOQ7h6ZXr16xatWqOOWUU+LMM8+M3XbbrfVjn/zkJ2Nn+cMf/hDLly+PAQMGRJcuXXbadQCACgg0M2fOjLq6uujZs2fcf//9ra9XVVW1K9AsWrQoxo8fH0uWLIlTTz212Gk41d2Sq666Ku6+++4iTL322mtx0003xbvf/e4operqquJRqWprqzcpv/W1StLc3FI8AMg00Nx6660dvmA6HmHMmDExbNiwuPbaa2PSpElFQBo9evRm6/zqV7+K//iP/yiOV+jRo0cRhn74wx/G1VdfHaWSgswevXaLqprK/QVeV9f2l3evXt2jR4/uUalamppjxauvCTUAue8U3NEVUmvWrClCSX19fYwdOzauvPLKLQaaNLw0ceLEIswkhxxySLGx37baSifQVgNNCjOvzPxFbHj5lahEjevXtSm/fNPMeL1L16hEtXs2RMMpHyvui5YWgYbOZXt+1kGu93KHAs173/vezQ4RPfHEE1usu2DBghg8eHARZpI0H2bx4sVbrHPYYYe1Pl+xYkXMmDGjQ8cs9O69e2yvFGY2vPBSVKING9oePrph2cuxobay5zI1NFRuDxWdk3uSStWhQJOGfjZau3Zt/Pa3v40bb7wxvvjFL261buqd6devX2s5BaPq6upiX5s0J2dL7rjjjvjmN78ZRxxxRDH3ZlstX746OvqP6Zqaaj8o2MQrrzRGU1NzWa7tnqSz3ZOwo6W+k/Z2RnQo0Oy7775tymly7vDhw4u5MaNGjdpi3Zqamk1WKHXt2rUIRlsLNGnCcZ8+fYp9cG677bb49Kc/vU3tTmHG6AA7mnuKzsY9SSXaYTNcUyh5+eWXt/p5KbSkYaM3a2xsLFZNbU0KQscee2z8zd/8TUyfPn272gsA7Do61EOT5q+8eQ5NmhT51FNPxYc+9KGt1h00aFDceeedreWlS5cWK5+21DuTNvFLJ3ufdNJJRTmFnzRMBQDQ4UCTNtR782Z3SZo/c/TRR2+17pAhQ4p5NGlib1rZNHXq1Bg6dGgxFJU26+vevXvx/M3222+/uOyyy4rhpne+853FfJ0TTjjB/0EAYNsCzbJly4o9Y5qamuK6664rXrvmmmti2rRpRQBJQeP6668vVi1tSW1tbfE+F198cVE/9bRs3NcmhZ20eV5alv1mxx13XJx//vkxbty44riF0047LT73uc+1t+kAwC6u3YHmq1/9anHEwUUXXVSUf/rTn8aPf/zjuOWWW4pVR9/97neLvWLSZN2tGTlyZMyaNSvmz59fLOFuaGgoXl+4cOFm6/z1X/918QAA6HCgmTdvXnHMQd++fYtemhRgUi9J6lVJ0plOaa5Le6XhoxEjRrT78wEANqfdM2sPOOCAmDt3bvH8X/7lX4r5Lp/97GdbP/7oo48WIQUAoNP20EyYMCEuvPDC4s807yXNf0lHEaRVSmnCburBSQdIAgB02kDz/ve/v9ghOB1TsPfeexfLqDfuP5Mm7V5++eVx8MEH78y2AgBs/7Ltbt26xcCBA9u8llY3vXnoCQCg1OxOBwBkT6ABALIn0AAA2RNoAIDsCTQAQPYEGgAgewINAJA9gQYAyJ5AQ7vVVlVH1f89T89SGQA6A7+RaLeuNbXxsX3fXYSZj+57YFEGoK2bb74hzjrrlOJPSkegYZucc9Chccsxnyz+BKCtdevWxQMP3BfNzc3x4IP3FWVKQ6ABgB1kw4Y3oqWluXieQk0qUxoCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZK8sgWbRokUxevToGDJkSEyePDlaWlq2Wud73/teHHnkkfEXf/EXceGFF8aaNWtK0lYAoPMreaBZv359jBkzJgYOHBgzZsyIxYsXx8yZM7dY55577omf/exnccMNN8TPf/7zos4Pf/jDkrUZAOjcSh5o5syZU/SujB8/Pvr37x9jx46N6dOnb7HOCy+8EFdffXUceuihsf/++8eoUaPi8ccfL1mbAYDOrbbUF1ywYEEMHjw46uvri/KAAQOKHpctOf/889uUn3nmmSLYbKuqqm2uAlvlvqKzcU92nr/7VPb/o+O25e+u5IEm9c7069evtVxVVRXV1dWxcuXK6Nmz51brpzAza9asuOuuu7b52r17777NdWBLGhq6l7sJ0IZ7sry6dava5PdOjx49ytaeSlLyQFNTUxNdunRp81rXrl1j7dq1Ww00zc3N8bWvfS1OO+20eM973rPN116+fHW0Y/7x26qpqfaDgk288kpjNDU1l+Xa7kk62z1JRGPjmk1+76xd28FfPETqoWlvZ0TJA00KLU8++WSb1xobG6Ourm6rdb///e8XPTmXXHJJh66dwkxHAw1sjnuKzsY92Xn+7v3e2YUnBQ8aNCjmzZvXWl66dGmx8mlrvTMPP/xwTJs2LaZMmdI6/wYAoCyBJu09k+bRpCXbydSpU2Po0KHFUNSqVauiqalpkzpp0vDFF18cX//612OvvfYqenRef/11/wcBgPIEmtra2pg0aVJMnDgxjjrqqJg9e3aMGzeuNeykTffe6ic/+Um89tprcemll8bhhx9ePE444YRSNx0A6KRKPocmGTlyZLFSaf78+cUS7oaGhuL1hQsXvu3np4nA6QEA0GkCTdKnT58YMWJEuS4PAOxCHE4JAGRPoAEAsifQAADZE2gAgOwJNABA9gQaACB7Ag0AkD2BBgDInkADAGRPoAEAsifQAADZE2gAgOyV7XBKAHY91dVVxaNS1dZWb1J+62uVpLm5pXiUgkADwA6Rgkyvhu5RU8GBpq6u7S/vXr26R48e3aNSNTW3xKuvNJYk1Ag0AOywQJPCzLfnPB/Pvbo+KtGGda+1KV9+/7NR23W3qET9enWJLw/ft7gvBBoAspPCzDMr1kYlal7f9ut+9pW1Ud2lcoecSsnfMgCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyV5ZAs2jRohg9enQMGTIkJk+eHC0tLe2q99hjj8Xxxx+/09sHAOSl5IFm/fr1MWbMmBg4cGDMmDEjFi9eHDNnztxqvd/97ndx0UUXxRtvvFGSdgIA+Sh5oJkzZ06sWbMmxo8fH/3794+xY8fG9OnTt1jntddeiy996Utx9tlnb9e1q6o6/oCdcV+5J9kZ3JN0NqW4r2qjxBYsWBCDBw+O+vr6ojxgwICil2ZLamtr4/bbb4/f//73Ra9OR/XuvXuH68LbaWjoXu4mQBvuSSr1nix5oEm9M/369WstV1VVRXV1daxcuTJ69uz5tnW6dOkSffv2LQLN9li+fHW0c7rOJmpqqv2gYBOvvNIYTU3NZbm2e5K3455kV7onUw9NezsjSh5oampqioDyZl27do21a9duNtDsKCnMdDTQwOa4p+hs3JNU4j1Z8jk0KbSsWLGizWuNjY1RV1dX6qYAALuIkgeaQYMGxbx581rLS5cuLVY+7ezeGQBg11XyQJP2nknzaDZO7p06dWoMHTq0GIpatWpVNDU1lbpJAEDmSh5o0oqlSZMmxcSJE+Ooo46K2bNnx7hx41rDTtp0DwBgW5R8UnAycuTImDVrVsyfP79Ywt3Q0FC8vnDhwi3WSwHo4YcfLlErAYBclCXQJH369IkRI0aU6/IAwC7E4ZQAQPYEGgAgewINAOwgVdW1fz6AqKr6T2VKQqABgB2kqrZLdBlwTBFmugwYXpQpDdERAHag3YacWjwoLT00AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AED2BBoAIHsCDQCQPYEGAMieQAMAZE+gAQCyV5ZAs2jRohg9enQMGTIkJk+eHC0tLVut88ADD8Sxxx4bw4YNi3vvvbck7QQA8lDyQLN+/foYM2ZMDBw4MGbMmBGLFy+OmTNnbjUAjRs3Li644IK48cYb47vf/W48/fTTJWszANC5lTzQzJkzJ9asWRPjx4+P/v37x9ixY2P69OlbrHPnnXfGUUcdFaeddloMGDAgzj777PjpT39asjYDAJ1bbakvuGDBghg8eHDU19cX5RRQUi/N1uoMHz68tXzooYfGddddt83Xrq6OaMfo1hbV7rVnVNWV/K+NTqamd68291U5HbDHQdG1tlt5G0HZ7f2Ofp3mnjywd9foWltV3kZQdvv27LLd92TVNtxGJf/NnHpn+vX78zdeVVVVVFdXx8qVK6Nnz55vW6exsbFNnR49esSyZcu2+dp77LF7bK+GT4zc7vdg19HQ0L3cTYgvDP1KuZtAJ9IZ7skLPrRPuZtABd6TJc/xNTU10aXLn1Nb0rVr11i7dm2762zt8wGAylLyQJN6YVasWLFJD0xdXV2762zt8wGAylLyQDNo0KCYN29ea3np0qXFyqfNDTe9XZ3HH388+vbtu9PbCgDkoeSBJu09k+bRpCXbydSpU2Po0KHFsNKqVauiqalpkzrHH3983HfffbFw4cKid+bWW28t9qMBAEiqWtqzq90ONnv27Lj44ouLuTBpQnAKKAcddFCx4unuu++OQw45ZJM61157bbEHTaqz//77x49//OPo1s3KDgCgTIEmeemll2L+/PnFEu6GhoZ21XnqqafixRdfLHp53jqxGACoXGULNAAAO4rDKQGA7Ak0AED2BBoga2mPquOOOy6ee+65cjcF4qGHHoqRI0fG+973vvirv/qrrR7tw44j0NBu6dTz0aNHF5OyJ0+eHKZf0RnCzJgxY+L5558vd1MglixZEl/72teKVbzpIOYDDjggLrvssnI3q2IINLRL2vww/eIYOHBgsYdQ+lfHzJkzy90sKtzYsWPjxBNPLHczoJB+LqYwM2rUqNhzzz3jU5/6VDzxxBPlblbFEGhol/SvjbQh4vjx46N///7FL5Lp06eXu1lUuIkTJ8a5555b7mZA4dhjj40zzjijtfzMM88U+6ZRGgIN7bJgwYJiz6D6+vqinDZBNDZMue23337lbgJstld72rRpceaZZ5a7KRVDoKFdUu9Mv379WstVVVXFLs8rV64sa7sAOqMpU6YU/wA87bTTyt2UilFb7gaQh3TW1lt3Z07HUKxdu3aLB4sCVJq5c+fGv/7rv8Ydd9wRdXV15W5OxdBDQ7uk0JJWlLxZOijUNyvAny1durSYGDxhwoTijEJKR6ChXQYNGhTz5s1r802bxoj1zgD8SeqxTqtB0z40H/3oR4t/9KWHLS5KQ6ChXdLeM2keTVqynUydOjWGDh1aDEUBEPGf//mfxSHKaajp8MMPb33YJ6k0HE5Ju82ePbvoSk1zZ9KE4FtvvVWXKgCdgkDDNnnppZdi/vz5xRLuhoaGcjcHAAoCDQCQPXNoAIDsCTQAQPYEGgAgewINAJA9gQYAyJ5AA2y3tOniF77whWI5/9FHHx3/+7//u13v99xzzxUnum/rxzYn7XJ9yimnxGGHHRaf/exn48UXX2zz8V/96ldx3HHHbVebgfISaIDtdtdddxV7FM2aNStuu+222Geffbbr/VL9Rx55ZIe07fXXX48LLrggzj777Ljvvvuie/fuMXHixDaf84EPfCDuueeeHXI9oDwEGmC7vfrqq3HwwQfHO9/5znj3u98de+6553a9X9qJ+h3veMcOadvixYtj1apVMXr06Nh7773jwgsvjKampjafU1tbGz169Ngh1wPKQ6ABOuznP/95Mfzzve99r+ilSc//8i//svjYo48+Gp/85CeLYahTTz21OOMmOeecc2L8+PHxwQ9+sDhKIz1PQ0HpaI2ODivdfffdccIJJ7SW04GAhx56aBFmUohJAen73/9+bNiwId73vvfF9ddfv9Uhp1Q3teHNj69+9autH58zZ06cdNJJccQRR8Rll11WHNYKlI9AA3TYxz72sWJo6POf/3yceOKJxfPp06dHc3Nz/O3f/m1x4vBDDz1UHG46efLkNoHl6quvjnvvvTcGDhxYhKB///d/73A7PvKRj8SSJUvi6aefbg0bBxxwQNFb1Lt377jmmmviRz/6UdHeFH7a413velfx9aRHGqrq1atXa1h79tlni2Gsc889t/h605yhG264ocPtB7afQAN0WF1dXTE0lA4s3fh849BN6rFJQeePf/xjMeTzzDPPtNYbNWpU68Gmp512WjFnJvWedFS65oc//OH4xS9+UZRTiErX2GhjYDr55JNjwoQJbcLV1oa90pybK664ouhlGjFiRPGxFHAOOeSQou0pOJ111lnx8MMPd7j9wPar3QHvAbBJGLjpppuK3ov99tuvGPZJvTYbpQD0ds+3x8c//vGYNm1anHfeefHLX/6y6CFK0oqmdevWRf/+/eNLX/pSHHnkkfGZz3ymGPpqz+Tl73znO8Wcm7/7u79rfe2FF16IJ554ohhuStLHd9tttx3ydQAdI9AAO1yak5LCTOrJSEM+KWCkU9p3pjQH5vLLL4+ZM2cW4SU9kvvvv7+Yn3PrrbcW5TT8lSYBp16jrQWa1O70daTeplRno7322iuOPfbYuPTSS4tyCmtpNRVQPoacgB0uTcpNUmhIk4OvuuqqaGlp2anXTENDw4cPj3/8x39sM9yU9sX59a9/XczXSb01U6ZMiT59+sSBBx64xfdLQ2WXXHJJEZLq6+uLryXtt5OkCcj/8z//U8yl6dKlSxGW0uRmoHz00AA7XJrPMmzYsGIzu379+sXpp58e//zP/xwvv/zyTr1uGnZK82jSnxul1UkpUKUgs2zZsmLuS1rxlILIlsydO7dYjv7moaa+ffsWE45T70+ah5MmNi9durRYUfWtb31rp35twJZVtezsfzYBlEAKFqnX5Cc/+Uncfvvt5W4OUGJ6aIBdwhe/+MVYsWJFfPvb3y53U4Ay0EMDAGTPpGAAIHsCDQCQPYEGAMieQAMAZE+gAQCyJ9AAANkTaACA7Ak0AEDk7v8DeeTlRM4CgBsAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 82
  },
  {
   "cell_type": "code",
   "id": "9ff2610c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:21.623799Z",
     "start_time": "2025-05-09T01:56:21.613013Z"
    }
   },
   "source": [
    "# 同一票号的乘客数量可能不同，可能也与乘客生存率有关系\n",
    "TickCountDict = full_data['Ticket'].value_counts()\n",
    "TickCountDict.head(20)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticket\n",
       "CA. 2343        11\n",
       "1601             8\n",
       "CA 2144          8\n",
       "347082           7\n",
       "S.O.C. 14879     7\n",
       "3101295          7\n",
       "PC 17608         7\n",
       "347077           7\n",
       "382652           6\n",
       "113781           6\n",
       "19950            6\n",
       "347088           6\n",
       "W./C. 6608       5\n",
       "349909           5\n",
       "220845           5\n",
       "4133             5\n",
       "16966            5\n",
       "PC 17757         5\n",
       "113503           5\n",
       "C.A. 34651       4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 83
  },
  {
   "cell_type": "code",
   "id": "1f045f13",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.193967Z",
     "start_time": "2025-05-09T01:56:21.626777Z"
    }
   },
   "source": [
    "full_data['TickCom'] = full_data['Ticket'].map(TickCountDict)\n",
    "full_data['TickCom'].head()\n",
    "#查看TickCom与Survived之间关系\n",
    "palette = sns.color_palette('husl', n_colors=full_data['TickCom'].nunique())\n",
    "sns.barplot(data=full_data, x='TickCom', y='Survived', palette=palette)\n",
    "# 当TickCom大小适中时，乘客生存率较高"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TickCom', ylabel='Survived'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 84
  },
  {
   "cell_type": "code",
   "id": "83c4a902",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.390884Z",
     "start_time": "2025-05-09T01:56:22.197234Z"
    }
   },
   "source": [
    "#按照TickCom大小，将TickGroup分为三类。\n",
    "def TickCountGroup(num):\n",
    "    if (num >= 2) & (num <= 4):\n",
    "        return 0\n",
    "    elif (num == 1) | ((num >= 5) & (num <= 8)):\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "\n",
    "\n",
    "#得到各位乘客TickGroup的类别\n",
    "full_data['TickGroup'] = full_data['TickCom'].map(TickCountGroup)\n",
    "#查看TickGroup与Survived之间关系\n",
    "palette = sns.color_palette('husl', n_colors=full_data['TickGroup'].nunique())\n",
    "sns.barplot(data=full_data, x='TickGroup', y='Survived', palette=palette)\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TickGroup', ylabel='Survived'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 85
  },
  {
   "cell_type": "code",
   "id": "492fdfe3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.408609Z",
     "start_time": "2025-05-09T01:56:22.392953Z"
    }
   },
   "source": [
    "# Age缺失值补充\n",
    "numeric_data = full_data.select_dtypes(include=[np.number])\n",
    "numeric_data[numeric_data['Age'].notnull()].corr()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.038354  0.025757 -0.055224  0.008942   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.064598 -0.035322  0.081629   \n",
       "Pclass         -0.038354 -0.338481  1.000000 -0.426422  0.060832  0.018322   \n",
       "Age             0.025757 -0.064598 -0.426422  1.000000 -0.259695 -0.144728   \n",
       "SibSp          -0.055224 -0.035322  0.060832 -0.259695  1.000000  0.373587   \n",
       "Parch           0.008942  0.081629  0.018322 -0.144728  0.373587  1.000000   \n",
       "Fare            0.018482  0.331805 -0.694466  0.162292  0.316025  0.327315   \n",
       "familyNum      -0.031437  0.016639  0.050027 -0.249921  0.861952  0.792296   \n",
       "familySize     -0.047985  0.108631 -0.067487 -0.198493  0.750411  0.687001   \n",
       "TickCom        -0.010350  0.064962 -0.078554 -0.194936  0.679444  0.647029   \n",
       "TickGroup       0.007477 -0.319278  0.308877 -0.010602 -0.021512 -0.089695   \n",
       "\n",
       "                 Fare  familyNum  familySize   TickCom  TickGroup  \n",
       "PassengerId  0.018482  -0.031437   -0.047985 -0.010350   0.007477  \n",
       "Survived     0.331805   0.016639    0.108631  0.064962  -0.319278  \n",
       "Pclass      -0.694466   0.050027   -0.067487 -0.078554   0.308877  \n",
       "Age          0.162292  -0.249921   -0.198493 -0.194936  -0.010602  \n",
       "SibSp        0.316025   0.861952    0.750411  0.679444  -0.021512  \n",
       "Parch        0.327315   0.792296    0.687001  0.647029  -0.089695  \n",
       "Fare         1.000000   0.386768    0.471865  0.584847  -0.399751  \n",
       "familyNum    0.386768   1.000000    0.869082  0.800556  -0.063174  \n",
       "familySize   0.471865   0.869082    1.000000  0.667312  -0.355482  \n",
       "TickCom      0.584847   0.800556    0.667312  1.000000  -0.111638  \n",
       "TickGroup   -0.399751  -0.063174   -0.355482 -0.111638   1.000000  "
      ],
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>familyNum</th>\n",
       "      <th>familySize</th>\n",
       "      <th>TickCom</th>\n",
       "      <th>TickGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.038354</td>\n",
       "      <td>0.025757</td>\n",
       "      <td>-0.055224</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.018482</td>\n",
       "      <td>-0.031437</td>\n",
       "      <td>-0.047985</td>\n",
       "      <td>-0.010350</td>\n",
       "      <td>0.007477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.064598</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.331805</td>\n",
       "      <td>0.016639</td>\n",
       "      <td>0.108631</td>\n",
       "      <td>0.064962</td>\n",
       "      <td>-0.319278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.038354</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.426422</td>\n",
       "      <td>0.060832</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>-0.694466</td>\n",
       "      <td>0.050027</td>\n",
       "      <td>-0.067487</td>\n",
       "      <td>-0.078554</td>\n",
       "      <td>0.308877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.025757</td>\n",
       "      <td>-0.064598</td>\n",
       "      <td>-0.426422</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.259695</td>\n",
       "      <td>-0.144728</td>\n",
       "      <td>0.162292</td>\n",
       "      <td>-0.249921</td>\n",
       "      <td>-0.198493</td>\n",
       "      <td>-0.194936</td>\n",
       "      <td>-0.010602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.055224</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.060832</td>\n",
       "      <td>-0.259695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373587</td>\n",
       "      <td>0.316025</td>\n",
       "      <td>0.861952</td>\n",
       "      <td>0.750411</td>\n",
       "      <td>0.679444</td>\n",
       "      <td>-0.021512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>-0.144728</td>\n",
       "      <td>0.373587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.327315</td>\n",
       "      <td>0.792296</td>\n",
       "      <td>0.687001</td>\n",
       "      <td>0.647029</td>\n",
       "      <td>-0.089695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.018482</td>\n",
       "      <td>0.331805</td>\n",
       "      <td>-0.694466</td>\n",
       "      <td>0.162292</td>\n",
       "      <td>0.316025</td>\n",
       "      <td>0.327315</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.386768</td>\n",
       "      <td>0.471865</td>\n",
       "      <td>0.584847</td>\n",
       "      <td>-0.399751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>familyNum</th>\n",
       "      <td>-0.031437</td>\n",
       "      <td>0.016639</td>\n",
       "      <td>0.050027</td>\n",
       "      <td>-0.249921</td>\n",
       "      <td>0.861952</td>\n",
       "      <td>0.792296</td>\n",
       "      <td>0.386768</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.869082</td>\n",
       "      <td>0.800556</td>\n",
       "      <td>-0.063174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>familySize</th>\n",
       "      <td>-0.047985</td>\n",
       "      <td>0.108631</td>\n",
       "      <td>-0.067487</td>\n",
       "      <td>-0.198493</td>\n",
       "      <td>0.750411</td>\n",
       "      <td>0.687001</td>\n",
       "      <td>0.471865</td>\n",
       "      <td>0.869082</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.667312</td>\n",
       "      <td>-0.355482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TickCom</th>\n",
       "      <td>-0.010350</td>\n",
       "      <td>0.064962</td>\n",
       "      <td>-0.078554</td>\n",
       "      <td>-0.194936</td>\n",
       "      <td>0.679444</td>\n",
       "      <td>0.647029</td>\n",
       "      <td>0.584847</td>\n",
       "      <td>0.800556</td>\n",
       "      <td>0.667312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.111638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TickGroup</th>\n",
       "      <td>0.007477</td>\n",
       "      <td>-0.319278</td>\n",
       "      <td>0.308877</td>\n",
       "      <td>-0.010602</td>\n",
       "      <td>-0.021512</td>\n",
       "      <td>-0.089695</td>\n",
       "      <td>-0.399751</td>\n",
       "      <td>-0.063174</td>\n",
       "      <td>-0.355482</td>\n",
       "      <td>-0.111638</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 86
  },
  {
   "cell_type": "code",
   "id": "0394298d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.426945Z",
     "start_time": "2025-05-09T01:56:22.410813Z"
    }
   },
   "source": [
    "# 案例中，我们主要探究相同姓氏的乘客是否存在明显的同组效应。\n",
    "# 提取两部分数据，分别查看其“姓氏”是否存在同组效应（因为性别和年龄与乘客生存率关系最为密切\n",
    "# 因此用这两个特征作为分类条件\n",
    "full_data['Surname'] = full_data['Name'].map(lambda x: x.split(',')[0].strip())\n",
    "SurNameDict = full_data['Surname'].value_counts()\n",
    "SurNameDict\n",
    "\n",
    "full_data['SurnameNum'] = full_data['Surname'].map(SurNameDict)\n",
    "\n",
    "# 12岁以上男性：找出男性中同姓氏均获救的部分\n",
    "MaleDf = full_data[(full_data['Sex'] == 'male') & (full_data['Age'] > 12) & (full_data['familyNum'] >= 2)]\n",
    "\n",
    "MSurNamDf = MaleDf['Survived'].groupby(MaleDf['Surname']).mean()\n",
    "MSurNamDf.head()\n",
    "MSurNamDf.value_counts()\n",
    "# 大多数同姓氏的男性存在“同生共死”的特点，因此利用该同组效应\n",
    "# 我们对生存率为1的姓氏里的男性数据进行修正，提升其预测为“可以幸存”的概率。"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0.0    90\n",
       "1.0    19\n",
       "0.5     3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 87
  },
  {
   "cell_type": "code",
   "id": "b06906f0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.442061Z",
     "start_time": "2025-05-09T01:56:22.429822Z"
    }
   },
   "source": [
    "# 女性及儿童同组效应\n",
    "FemChildDf = full_data[((full_data['Sex'] == 'female') | (full_data['Age'] <= 12)) & (full_data['familyNum'] >= 2)]\n",
    "\n",
    "FCSurNamDf = FemChildDf['Survived'].groupby(FemChildDf['Surname']).mean()\n",
    "FCSurNamDf.head()\n",
    "FCSurNamDf.value_counts()\n",
    "# 同一姓氏的有115人全部存活 救援优先分配给女性和儿童"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "1.000000    115\n",
       "0.000000     27\n",
       "0.750000      2\n",
       "0.333333      1\n",
       "0.142857      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 88
  },
  {
   "cell_type": "code",
   "id": "51622067",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.457047Z",
     "start_time": "2025-05-09T01:56:22.443795Z"
    }
   },
   "source": [
    "# 获得生存率为1的姓氏\n",
    "MSurNamDict = MSurNamDf[MSurNamDf.values == 1].index\n",
    "MSurNamDict\n",
    "\n",
    "# 活得生存率为0的姓氏\n",
    "FCSurNamDict = FCSurNamDf[FCSurNamDf.values == 0].index\n",
    "FCSurNamDict\n",
    "\n",
    "#对数据集中这些姓氏的男性数据进行修正：1、性别改为女；2、年龄改为5\n",
    "full_data.loc[(full_data['Survived'].isnull()) & (full_data['Surname'].isin(MSurNamDict)) & (\n",
    "        full_data['Sex'] == 'male'), 'Sex'] = 'female'\n",
    "full_data.loc[(full_data['Survived'].isnull()) & (full_data['Surname'].isin(MSurNamDict)) & (\n",
    "        full_data['Sex'] == 'male'), 'Age'] = 5\n",
    "\n",
    "#对数据集中这些姓氏的女性及儿童的数据进行修正：1、性别改为男；2、年龄改为60。\n",
    "full_data.loc[(full_data['Survived'].isnull()) & (full_data['Surname'].isin(FCSurNamDict)) & (\n",
    "        (full_data['Sex'] == 'female') | (full_data['Age'] <= 12)), 'Sex'] = 'male'\n",
    "full_data.loc[(full_data['Survived'].isnull()) & (full_data['Surname'].isin(FCSurNamDict)) & (\n",
    "        (full_data['Sex'] == 'female') | (full_data['Age'] <= 12)), 'Age'] = 60\n"
   ],
   "outputs": [],
   "execution_count": 89
  },
  {
   "cell_type": "code",
   "id": "9dd155be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.469458Z",
     "start_time": "2025-05-09T01:56:22.458611Z"
    }
   },
   "source": [
    "fullSel = full_data[['Survived', 'Age', 'Fare', 'Parch', 'Pclass', 'SibSp', 'familyNum', 'familySize',\n",
    "                     'TickCom', 'TickGroup']]\n",
    "#查看各特征与标签的相关性\n",
    "corrDf = pd.DataFrame()\n",
    "corrDf = fullSel.corr()\n",
    "corrDf['Survived'].sort_values(ascending=True)\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       -0.338481\n",
       "TickGroup    -0.319278\n",
       "Age          -0.064598\n",
       "SibSp        -0.035322\n",
       "familyNum     0.016639\n",
       "TickCom       0.064962\n",
       "Parch         0.081629\n",
       "familySize    0.108631\n",
       "Fare          0.331805\n",
       "Survived      1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 90
  },
  {
   "cell_type": "code",
   "id": "f52c8356fd499c81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.896128Z",
     "start_time": "2025-05-09T01:56:22.471184Z"
    }
   },
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "sns.heatmap(fullSel[['Survived', 'Age', 'Fare', 'Parch', 'Pclass', 'SibSp', 'familyNum', 'familySize',\n",
    "                     'TickCom', 'TickGroup']].corr(), cmap='BrBG', annot=True,\n",
    "            linewidths=.5)\n",
    "_ = plt.xticks(rotation=45)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 91
  },
  {
   "cell_type": "code",
   "id": "513adb9052dbe519",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.914012Z",
     "start_time": "2025-05-09T01:56:22.899207Z"
    }
   },
   "source": [
    "# 根据相关系数 删除相关系数低的属性\n",
    "fullSel = fullSel.drop(['Age', 'Parch', 'SibSp', 'familyNum', 'TickCom'], axis=1)\n",
    "#one-hot编码\n",
    "fullSel = pd.get_dummies(fullSel)\n",
    "fullSel.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Survived      Fare  Pclass  familySize  TickGroup\n",
       "0       0.0  1.981001       3           1          1\n",
       "1       1.0  4.266662       1           1          0\n",
       "2       1.0  2.070022       3           0          1\n",
       "3       1.0  3.972177       1           1          0\n",
       "4       0.0  2.085672       3           0          1"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>familySize</th>\n",
       "      <th>TickGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 92
  },
  {
   "cell_type": "code",
   "id": "8e84b879f92bcee7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.955488Z",
     "start_time": "2025-05-09T01:56:22.917363Z"
    }
   },
   "source": [
    "dummies_title = pd.get_dummies(full_data['Title'],prefix='Title')\n",
    "dummies_Sex = pd.get_dummies(full_data['Sex'], prefix='Sex')\n",
    "dummies_Pclass = pd.get_dummies(full_data['Pclass'], prefix='Pclass')\n",
    "last_data = pd.concat([fullSel,dummies_title ,dummies_Sex, dummies_Pclass], axis=1)\n",
    "last_data"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      Survived      Fare  Pclass  familySize  TickGroup  Title_Master  \\\n",
       "0          0.0  1.981001       3           1          1         False   \n",
       "1          1.0  4.266662       1           1          0         False   \n",
       "2          1.0  2.070022       3           0          1         False   \n",
       "3          1.0  3.972177       1           1          0         False   \n",
       "4          0.0  2.085672       3           0          1         False   \n",
       "...        ...       ...     ...         ...        ...           ...   \n",
       "1304       NaN  2.085672       3           0          1         False   \n",
       "1305       NaN  4.690430       1           0          0         False   \n",
       "1306       NaN  1.981001       3           0          1         False   \n",
       "1307       NaN  2.085672       3           0          1         False   \n",
       "1308       NaN  3.107198       3           1          0          True   \n",
       "\n",
       "      Title_Miss  Title_Mr  Title_Mrs  Title_Officer  Title_Royalty  \\\n",
       "0          False      True      False          False          False   \n",
       "1          False     False       True          False          False   \n",
       "2           True     False      False          False          False   \n",
       "3          False     False       True          False          False   \n",
       "4          False      True      False          False          False   \n",
       "...          ...       ...        ...            ...            ...   \n",
       "1304       False      True      False          False          False   \n",
       "1305       False     False      False          False           True   \n",
       "1306       False      True      False          False          False   \n",
       "1307       False      True      False          False          False   \n",
       "1308       False     False      False          False          False   \n",
       "\n",
       "      Sex_female  Sex_male  Pclass_1  Pclass_2  Pclass_3  \n",
       "0          False      True     False     False      True  \n",
       "1           True     False      True     False     False  \n",
       "2           True     False     False     False      True  \n",
       "3           True     False      True     False     False  \n",
       "4          False      True     False     False      True  \n",
       "...          ...       ...       ...       ...       ...  \n",
       "1304       False      True     False     False      True  \n",
       "1305        True     False      True     False     False  \n",
       "1306       False      True     False     False      True  \n",
       "1307       False      True     False     False      True  \n",
       "1308       False      True     False     False      True  \n",
       "\n",
       "[1309 rows x 16 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>familySize</th>\n",
       "      <th>TickGroup</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.690430</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.107198</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 16 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 93
  },
  {
   "cell_type": "code",
   "id": "d13f7ed8c986af0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:22.974122Z",
     "start_time": "2025-05-09T01:56:22.957722Z"
    }
   },
   "source": [
    "train_data = last_data[last_data['Survived'].notnull()]\n",
    "test_data = last_data[last_data['Survived'].isnull()]\n",
    "# 提取出特征\n",
    "train_data_X = train_data.drop('Survived', axis=1)\n",
    "train_data_y = train_data['Survived']\n",
    "\n",
    "test_data_X = test_data.drop('Survived', axis=1)\n",
    "test_data_y = test_data['Survived']\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=10)\n",
    "\n",
    "classifiers = []\n",
    "classifiers.append(SVC())\n",
    "classifiers.append(DecisionTreeClassifier())\n",
    "classifiers.append(RandomForestClassifier())\n",
    "classifiers.append(ExtraTreesClassifier())\n",
    "classifiers.append(GradientBoostingClassifier())\n",
    "classifiers.append(KNeighborsClassifier())\n",
    "classifiers.append(LogisticRegression())\n",
    "classifiers.append(LinearDiscriminantAnalysis())\n",
    "classifiers.append(XGBClassifier())\n",
    "\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 94
  },
  {
   "cell_type": "code",
   "id": "9077483f379088ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:37.156010Z",
     "start_time": "2025-05-09T01:56:22.977024Z"
    }
   },
   "source": [
    "#不同机器学习交叉验证结果汇总\n",
    "cv_results = []\n",
    "for classifier in classifiers:\n",
    "    cv_results.append(cross_val_score(classifier, train_data_X, train_data_y,\n",
    "                                      scoring='accuracy', cv=kfold, n_jobs=-1))\n",
    "\n",
    "#求出模型得分的均值和标准差\n",
    "cv_means = []\n",
    "cv_std = []\n",
    "for cv_result in cv_results:\n",
    "    cv_means.append(cv_result.mean())\n",
    "    cv_std.append(cv_result.std())\n",
    "\n",
    "#汇总数据\n",
    "cvResDf = pd.DataFrame({'cv_mean': cv_means,\n",
    "                        'cv_std': cv_std,\n",
    "                        'algorithm': ['SVC', 'DecisionTreeCla', 'RandomForestCla', 'ExtraTreesCla',\n",
    "                                      'GradientBoostingCla', 'KNN', 'LR', 'LDA', 'Xgboost']})\n",
    "\n",
    "cvResDf\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    cv_mean    cv_std            algorithm\n",
       "0  0.835019  0.035179                  SVC\n",
       "1  0.819326  0.025169      DecisionTreeCla\n",
       "2  0.814844  0.030458      RandomForestCla\n",
       "3  0.812584  0.029217        ExtraTreesCla\n",
       "4  0.835044  0.041628  GradientBoostingCla\n",
       "5  0.840674  0.044960                  KNN\n",
       "6  0.827179  0.034782                   LR\n",
       "7  0.827191  0.036129                  LDA\n",
       "8  0.835069  0.035942              Xgboost"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cv_mean</th>\n",
       "      <th>cv_std</th>\n",
       "      <th>algorithm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.835019</td>\n",
       "      <td>0.035179</td>\n",
       "      <td>SVC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.819326</td>\n",
       "      <td>0.025169</td>\n",
       "      <td>DecisionTreeCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.814844</td>\n",
       "      <td>0.030458</td>\n",
       "      <td>RandomForestCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.812584</td>\n",
       "      <td>0.029217</td>\n",
       "      <td>ExtraTreesCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.835044</td>\n",
       "      <td>0.041628</td>\n",
       "      <td>GradientBoostingCla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.840674</td>\n",
       "      <td>0.044960</td>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.827179</td>\n",
       "      <td>0.034782</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.827191</td>\n",
       "      <td>0.036129</td>\n",
       "      <td>LDA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.835069</td>\n",
       "      <td>0.035942</td>\n",
       "      <td>Xgboost</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 95
  },
  {
   "cell_type": "code",
   "id": "b104286e744a81f5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:56:37.388998Z",
     "start_time": "2025-05-09T01:56:37.159164Z"
    }
   },
   "source": [
    "palette = sns.color_palette('husl', n_colors=cvResDf['algorithm'].nunique())\n",
    "bar = sns.barplot(data=cvResDf.sort_values(by='cv_mean', ascending=False), x='cv_mean', y='algorithm', palette=palette)\n",
    "bar.set(xlim=(0.5, 0.9))\n",
    "# GradientBoostingCla,DecisionTreeCla,ExtraTreesCla,SVC 表现的好"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.5, 0.9)]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 96
  },
  {
   "cell_type": "code",
   "id": "de26c9b349deac16",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:21.629137Z",
     "start_time": "2025-05-09T01:56:37.391230Z"
    }
   },
   "source": [
    "# 模型调优\n",
    "GBC = GradientBoostingClassifier()\n",
    "gb_param_grid_GBC = {\n",
    "    'n_estimators': [100, 200, 300, 400],\n",
    "    'learning_rate': [0.1, 0.05, 0.01, 0.5],\n",
    "    'max_depth': [4, 8, 10],\n",
    "    'min_samples_leaf': [100, 150],\n",
    "    'max_features': [0.3, 0.1]\n",
    "}\n",
    "modelgsGBC = GridSearchCV(GBC, param_grid=gb_param_grid_GBC, cv=kfold,\n",
    "                          scoring=\"accuracy\", n_jobs=-1, verbose=1)\n",
    "modelgsGBC.fit(train_data_X, train_data_y)\n",
    "modelgsGBC.best_score_\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 192 candidates, totalling 1920 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(0.8496504369538078)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 97
  },
  {
   "cell_type": "code",
   "id": "f9427d673ff1716",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:24.875651Z",
     "start_time": "2025-05-09T01:57:21.631874Z"
    }
   },
   "source": [
    "Knn = KNeighborsClassifier()\n",
    "gb_param_grid_Knn = {\n",
    "    'n_neighbors': np.arange(1,20), \n",
    "    'weights': ['uniform', 'distance'],  \n",
    "    'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],  \n",
    "    'p': [1, 2]  \n",
    "}\n",
    "\n",
    "grid_search_knn = GridSearchCV(\n",
    "    estimator=Knn,\n",
    "    param_grid=gb_param_grid_Knn,\n",
    "    cv=kfold,\n",
    "    scoring='accuracy',\n",
    "    n_jobs=-1,\n",
    "    verbose=2\n",
    ")\n",
    "grid_search_knn.fit(train_data_X, train_data_y)\n",
    "\n",
    "# 输出最佳参数\n",
    "print(\"最佳参数组合：\", grid_search_knn.best_params_)\n",
    "print(\"验证集最高准确率：\", grid_search_knn.best_score_)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 304 candidates, totalling 3040 fits\n",
      "最佳参数组合： {'algorithm': 'auto', 'n_neighbors': np.int64(5), 'p': 1, 'weights': 'uniform'}\n",
      "验证集最高准确率： 0.8417977528089887\n"
     ]
    }
   ],
   "execution_count": 98
  },
  {
   "cell_type": "code",
   "id": "daa0c434246a8720",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:27.005603Z",
     "start_time": "2025-05-09T01:57:24.877793Z"
    }
   },
   "source": [
    "XGC = XGBClassifier(random_state=42)\n",
    "param_grid_XGC = {\n",
    "    'n_estimators': [50, 100, 150],\n",
    "    'learning_rate': [0.01, 0.1, 0.3],\n",
    "    'max_depth': [3, 5, 7]\n",
    "}\n",
    "grid_search_XGC = GridSearchCV(XGC, param_grid_XGC, cv=kfold, scoring='accuracy', n_jobs=-1)\n",
    "grid_search_XGC.fit(train_data_X, train_data_y)\n",
    "print(\"最佳参数：\", grid_search_XGC.best_params_)\n",
    "print(grid_search_XGC.best_score_)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数： {'learning_rate': 0.3, 'max_depth': 5, 'n_estimators': 50}\n",
      "0.8485642946317103\n"
     ]
    }
   ],
   "execution_count": 99
  },
  {
   "cell_type": "code",
   "id": "b244eb6c4aaf6f0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:33.236479Z",
     "start_time": "2025-05-09T01:57:27.008501Z"
    }
   },
   "source": [
    "svc = SVC(random_state=42,probability=True)\n",
    "gb_param_grid_SVC = {'C': [0.1, 0.5, 1, 2, 3, 5, 10],\n",
    "                     'gamma': ['scale', 0.1, 0.01],\n",
    "                     'kernel': ['rbf', 'poly', 'sigmoid']\n",
    "                     }\n",
    "modelgsSVC = GridSearchCV(svc, param_grid=gb_param_grid_SVC, cv=kfold,\n",
    "                          scoring=\"accuracy\", n_jobs=-1, verbose=1)\n",
    "modelgsSVC.fit(train_data_X, train_data_y)\n",
    "modelgsSVC.best_score_\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 63 candidates, totalling 630 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "np.float64(0.8350187265917602)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 100
  },
  {
   "cell_type": "markdown",
   "id": "fc6372de",
   "metadata": {},
   "source": [
    "模型评估"
   ]
  },
  {
   "cell_type": "code",
   "id": "2c0228cf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:33.245401Z",
     "start_time": "2025-05-09T01:57:33.241936Z"
    }
   },
   "source": [
    "from sklearn.metrics import auc,roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ],
   "outputs": [],
   "execution_count": 101
  },
  {
   "cell_type": "code",
   "id": "47a88e1a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:33.442656Z",
     "start_time": "2025-05-09T01:57:33.247055Z"
    }
   },
   "source": [
    "models = [modelgsGBC, grid_search_knn, grid_search_XGC, modelgsSVC]\n",
    "model_names = ['GBC', 'KNN', 'XGC', 'SVC']\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# 循环绘制 ROC 曲线\n",
    "for model, name in zip(models, model_names):\n",
    "    y_score = model.predict_proba(train_data_X)[:, 1]\n",
    "    fpr, tpr, thresholds = roc_curve(train_data_y, y_score)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, lw=2, label=f'{name} ROC curve (area = {roc_auc:.2f})')\n",
    "\n",
    "# 绘制对角线\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "\n",
    "# 设置坐标轴范围和标签\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('假正例率')\n",
    "plt.ylabel('真正率')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 102
  },
  {
   "cell_type": "code",
   "id": "97d05834",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:33.540456Z",
     "start_time": "2025-05-09T01:57:33.444754Z"
    }
   },
   "source": [
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "def compare_confusion_matrices(models, model_names, test_data_X, test_data_y):\n",
    "    metrics = []\n",
    "    for model, name in zip(models, model_names):\n",
    "        y_pred = model.predict(test_data_X)\n",
    "        cm = confusion_matrix(test_data_y, y_pred)\n",
    "        precision = precision_score(test_data_y, y_pred)\n",
    "        recall = recall_score(test_data_y, y_pred)\n",
    "        f1 = f1_score(test_data_y, y_pred)\n",
    "        metrics.append((name, cm, precision, recall, f1))\n",
    "        print(f\"{name}混淆矩阵:\\n{cm}\")\n",
    "        print(f\"{name}精确率: {precision:.2f}, 召回率: {recall:.2f}, F1值: {f1:.2f}\\n\")\n",
    "    \n",
    "    # 假设KNN在指标上最优，找出它\n",
    "    best_model = max(metrics, key=lambda x: (x[2] + x[3] + x[4]) / 3)  # 简单平均指标比较\n",
    "\n",
    "# 执行比较\n",
    "models = [modelgsGBC, grid_search_knn, grid_search_XGC, modelgsSVC]\n",
    "model_names = ['GBC', 'KNN', 'XGC', 'SVC']\n",
    "compare_confusion_matrices(models, model_names, train_data_X, train_data_y)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GBC混淆矩阵:\n",
      "[[503  46]\n",
      " [ 65 277]]\n",
      "GBC精确率: 0.86, 召回率: 0.81, F1值: 0.83\n",
      "\n",
      "KNN混淆矩阵:\n",
      "[[498  51]\n",
      " [ 69 273]]\n",
      "KNN精确率: 0.84, 召回率: 0.80, F1值: 0.82\n",
      "\n",
      "XGC混淆矩阵:\n",
      "[[512  37]\n",
      " [ 57 285]]\n",
      "XGC精确率: 0.89, 召回率: 0.83, F1值: 0.86\n",
      "\n",
      "SVC混淆矩阵:\n",
      "[[492  57]\n",
      " [ 90 252]]\n",
      "SVC精确率: 0.82, 召回率: 0.74, F1值: 0.77\n",
      "\n"
     ]
    }
   ],
   "execution_count": 103
  },
  {
   "cell_type": "code",
   "id": "944846b8f2c369f9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:33.586504Z",
     "start_time": "2025-05-09T01:57:33.544163Z"
    }
   },
   "source": [
    "y_ =grid_search_knn.predict(test_data_X)\n",
    "y_ = y_.astype(int)\n",
    "#导出预测结果\n",
    "ResultDf=pd.DataFrame()\n",
    "ResultDf['PassengerId']=full_data['PassengerId'][full_data['Survived'].isnull()]\n",
    "ResultDf['Survived']= y_\n",
    "ResultDf\n",
    "#将预测结果导出为csv文件\n",
    "ResultDf.to_csv('./zxy_knn.csv',index=False)\n",
    "display(ResultDf.head())"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     PassengerId  Survived\n",
       "891          892         0\n",
       "892          893         1\n",
       "893          894         0\n",
       "894          895         0\n",
       "895          896         0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>892</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 104
  },
  {
   "cell_type": "code",
   "id": "1d91c21f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-09T01:57:33.592930Z",
     "start_time": "2025-05-09T01:57:33.589078Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": 104
  }
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